A STUDY OF PREDICTED ENERGY SAVINGS AND SENSITIVITY ANALYSIS. A Thesis YING YANG

Transcription

1 A STUDY OF PREDICTED ENERGY SAVINGS AND SENSITIVITY ANALYSIS A Thesis by YING YANG Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Chair of Committee, Committee Members, Head of Department, Charles H. Culp Wei Yan Sarel Lavy Ward V. Wells August 2013 Major Subject: Architecture Copyright 2013 Ying Yang

2 ABSTRACT The sensitivity of the important inputs and the savings prediction function reliability for the WinAM 4.3 software is studied in this research. WinAM was developed by the Continuous Commissioning (CC ) group in the Energy Systems Laboratory at Texas A&M University. For the sensitivity analysis task, fourteen inputs are studied by adjusting one input at a time within ± 3 compared with its baseline. The Single Duct Variable Air Volume (SDVAV) system with and without the economizer has been applied to the square zone model. Mean Bias Error (MBE) and Influence Coefficient (IC) have been selected as the statistical methods to analyze the outputs that are obtained from WinAM 4.3. For the saving prediction reliability analysis task, eleven Continuous Commissioning projects have been selected. After reviewing each project, seven of the eleven have been chosen. The measured energy consumption data for the seven projects is compared with the simulated energy consumption data that has been obtained from WinAM 4.3. Normalization Mean Bias Error (NMBE) and Coefficient of Variation of the Root Mean Squared Error (CV (RMSE)) statistical methods have been used to analyze the results from real measured data and simulated data. Highly sensitive parameters for each energy resource of the system with the economizer and the system without the economizer have been generated in the sensitivity analysis task. The main result of the savings prediction reliability analysis is that calibration improves the model s quality. It also improves the predicted energy savings results compared with the results generated from the uncalibrated model. ii

3 ACKNOWLEDGEMENTS I would like to express my very great appreciation to Professor Charles H. Culp, for his valuable suggestions, patience and understanding during the planning and development of this research work. I would like to express my deep gratitude to Professor Wei Yan and Professor Sarel Lavy for their consideration and patience to me during this research. One of the Chinese old saying s He who teaches me for one day is my father for life. I really appreciate them that they are my Professors. I would like to offer my special thanks to Kevin Christman, Dr. Charles Lindsey, and Mrs. Bonita Culp. The advice given by them has been a great help in this research. I would like to thank the Energy Systems Laboratory for financially supporting me as I finished this research. I would also like to extend my thanks to the co-workers in the ESL and the staff on campus. Finally, I want to thank my parents for their support and encouragement throughout my study. iii

4 NOMENCLATURE APE ASHRAE CC CV (RMSE) DSA EP FP IC IP Max MCA Min NLPL NMBE OA OAT Occ Roof U SA SDVAV Absolute Percentage Error American Society of Heating, Refrigerating and Air- Conditioning Engineers Continuous Commissioning Coefficient of Variation of the Root Mean Squared Error Differential Sensitivity Analysis Error Percentage VAV fan power Influence Coefficient Interior zone percentage Maximum airflow Monte Carlo Analysis Minimum airflow Nighttime lighting and plug load ratio Normalized Mean Bias Error Outside air percentage Outside air temperature Peak occupancy Roof U-value Sensitivity Analysis Single Duct Variable Air Volume iv

5 SHGC SSA Tc Tz UA Wall U Window U W-W Solar Heat Gain Coefficient Stochastic sensitivity analysis Cooling coil temperature Zone temperature Uncertainty Analysis Exterior wall U-value Exterior window U-value Window-wall ratio v

6 TABLE OF CONTENTS Page ABSTRACT...ii ACKNOWLEDGEMENTS... iii NOMENCLATURE... iv TABLE OF CONTENTS... vi LIST OF FIGURES... ix LIST OF TABLES... xv 1. INTRODUCTION LITERATURE REVIEW Statistical methods for simulated model calibration Sensitivity Analysis (SA) and Uncertainty Analysis (UA) METHODOLOGIES Sensitivity analysis of WinAM s input parameters Predicted savings potential reliability analysis INPUTS PROPERTIES ANALYSIS Outside air percentage (OA) Interior zone percentage (IP) Window-wall ratio (W-W) Minimum airflow rate (Min) Maximum airflow rate (Max) Zone temperature (Tz) Cooling coil temperature (Tc) Average lighting energy consumption (Lighting) VAV fan power (FP) Nighttime lighting and plug load ratio (NLPL) Wall R-value (Wall R) Window U-value (Window U) vi

7 vii Page 4.13 Roof U-value (Roof U) Peak occupancy (Occ) CALIBRATION TASK Austin City Hall Dallas/Fort Worth (DFW) International Airport Terminal D DFW International Airport Rent-A-Car Center DFW Terminal E Sunset Valley Elementary School DFW International Airport North Business Tower Blanchfield Army Community Hospital, Fort Campbell, Kentucky RESULTS Introduction Results for sensitivity analysis Reliability of savings predictions CONCLUSIONS Conclusion for sensitivity analysis Conclusion for predicted energy savings analysis Future work REFERENCES APPENDIX A A.1 Convert the.bin weather file into a.ft weather file A.2 Convert the.ft weather file into an.epw weather file A.3 Convert.epw weather file into a.cvs weather file A.4 Convert edited.cvs file back into an.epw file A.5 Convert edited.epw weather file into a.bin weather file APPENDIX B B.1 Austin City Hall B.2 Dallas/Fort Worth (DFW) International Airport Terminal D B.3 DFW International Airport Rent-A-Car Center B. 4 DFW international airport Terminal E B. 5 Sunset Valley Elementary School B.6 DFW International Airport North Business Tower

8 Page B.7 Blanchfield Army Community Hospital APPENDIX C viii

9 LIST OF FIGURES Page Figure 3-1 SDVAV System in WinAM Figure 3-2 Flowchart of savings prediction Figure 3-3 Weather data tool from CC-Compass Figure 4-1 Chilled water consumption for the OA% parameter, SDVAV with the economizer Figure 4-2 Chilled water consumption for the OA% parameter, SDVAV without the economizer Figure 4-3 Chilled water consumption IC value for the parameter OA% Figure 4-4 Interior zone and exterior zone Figure 4-5 Electric consumption for the parameter IP, SDVAV without the economizer Figure 4-6 Electric consumption IC value for the parameter IP Figure 4-7 Chilled water consumption for the parameter IP when SDVAV without the economizer Figure 4-8 Chilled water consumption IC value for the parameter IP Figure 4-9 Hot water consumption for the parameter IP, SDVAV without the economizer Figure 4-10 Hot water consumption IC value for the parameter IP Figure 4-11 Electric consumption for the parameter W-W, SDVAV with the economizer Figure 4-12 Electric consumption IC value for the parameter W-W Figure 4-13 Chilled water consumption for the parameter W-W, SDVAV with the economizer Figure 4-14 Chilled water consumption IC value for the parameter W-W ix

10 x Page Figure 4-15 Hot water consumption for the parameter W-W SDVAV with the economizer Figure 4-16 Hot water consumption IC value for the parameter W-W Figure 4-17 Electric consumption for the parameter Min, SDVAV without the economizer Figure 4-18 Electric consumption IC value for the parameter Min Figure 4-19 Chilled water consumption for the parameter Min, SDVAV without the economizer Figure 4-20 Chilled water consumption IC value for the parameter Min Figure 4-21 Hot water consumption for the parameter Min, SDVAV without the economizer Figure 4-22 Hot water consumption IC value for the parameter Min Figure 4-23 Electric consumption for the parameter Max, SDVAV with the economizer Figure 4-24 Electric consumption IC value for the parameter Max Figure 4-25 Chilled water consumption for the parameter Max, SDVAV with the economizer Figure 4-26 Chilled water consumption IC value for the parameter Max Figure 4-27 Hot water consumption for the parameter Max, SDVAV with the economizer Figure 4-28 Electric consumption for the parameter Tz, SDVAV with the economizer Figure 4-29 Electric consumption IC value for the parameter Tz Figure 4-30 Chilled water consumption for the parameter Tz, SDVAV with the economizer Figure 4-31 Chilled water consumption for the parameter Tz, SDVAV without the economizer... 53

11 xi Page Figure 4-32 Chilled water consumption IC value for the parameter Tz Figure 4-33 Hot water consumption for the parameter Tz, SDVAV with the economizer Figure 4-34 Hot water consumption IC value for the parameter Tz Figure 4-35 Electric consumption for the parameter Tc, SDVAV with the economizer Figure 4-36 Electric consumption IC value for the parameter Tc Figure 4-37 Chilled water consumption for the parameter Tc, SDVAV with the economizer Figure 4-38 Chilled water consumption for the parameter Tc, SDVAV without the economizer Figure 4-39 Chilled water consumption IC value for the parameter Tc Figure 4-40 Hot water consumption for the parameter Tc, SDVAV with the economizer Figure 4-41 Hot water consumption IC value for the parameter Tc Figure 4-42 Electric consumption for the parameter Lighting, SDVAV with the economizer Figure 4-43 Electric consumption IC value for the parameter Lighting Figure 4-44 Chilled water consumption for the parameter Lighting, SDVAV with the economizer Figure 4-45 Chilled water consumption IC value for the parameter Lighting Figure 4-46 Hot water consumption for the parameter Tz, SDVAV with the economizer Figure 4-47 Hot water consumption IC value for the parameter Lighting Figure 4-48 Electric consumption for the parameter FP, SDVAV with the economizer Figure 4-49 Electric consumption IC value for the parameter FP... 69

12 xii Page Figure 4-50 Chilled water consumption for the parameter FP, SDVAV with the economizer Figure 4-51 Chilled water consumption for the parameter FP, SDVAV without the economizer Figure 4-52 Chilled water consumption IC value for the parameter FP Figure 4-53 Electric consumption for the parameter NLPL, SDVAV with the economizer Figure 4-54 Electric consumption IC value for the parameter NLPL Figure 4-55 Chilled water consumption for the parameter NLPL, SDVAV with the economizer Figure 4-56 Chilled water consumption IC value for the parameter NLPL Figure 4-57 Hot water consumption for the parameter NLPL, SDVAV with the economizer Figure 4-58 Hot water consumption IC value for the parameter NLPL Figure 4-59 Electric consumption for the parameter Wall R, SDVAV with the economizer Figure 4-60 Chilled water consumption for the parameter Wall R, SDVAV with the economizer Figure 4-61 Hot water consumption for the parameter Wall R, SDVAV with the economizer Figure 4-62 Electric consumption for the parameter Window U, SDVAV with the economizer Figure 4-63 Electric consumption IC value for the parameter Window U Figure 4-64 Chilled water consumption for the parameter Window U, SDVAV with the economizer Figure 4-65 Chilled water consumption IC value for the parameter Window U Figure 4-66 Hot water consumption for the parameter Window U, SDVAV with the economizer... 84

13 xiii Page Figure 4-67 Hot water consumption IC value for the parameter Window U Figure 4-68 Electric consumption for the parameter Roof U, SDVAV with the economizer Figure 4-69 Electric consumption IC value for the parameter Roof U Figure 4-70 Chilled water consumption for the parameter Roof U, SDVAV with the economizer Figure 4-71 Chilled water consumption for the parameter Roof U, SDVAV without the economizer Figure 4-72 Chilled water consumption IC value for the parameter Roof U Figure 4-73 Hot water consumption for the parameter Roof U, SDVAV with the economizer Figure 4-74 Hot water consumption IC value for the parameter Roof U Figure 4-75 Electric consumption for the parameter Occ, SDVAV with the economizer Figure 4-76 Electric consumption IC value for the parameter Occ Figure 4-77 Chilled water consumption for the parameter Occ, SDVAV system with the economizer Figure 4-78 Chilled water consumption IC value for the parameter Occ Figure 4-79 Hot water consumption for the parameter Occ, SDVAV system with the economizer Figure 4-80 Hot water consumption IC value for the parameter Occ Figure 5-1 equest 3.64 zone assignment Figure 5-2 Internal structure of the north face of Terminal D Figure 5-3 Internal structure of the west face of Terminal D Figure 5-4 Floor plan of Sunset Valley Elementary School Figure 5-5 Overhead view of Blanchfield Army Community Hospital

14 Page Figure 5-6 Dimensions of each building in Blanchfield Army Community Hospital. 137 Figure 5-7 SDVAV system for real CC project Figure 5-8 SDVAV system in WinAM Figure 5-9 SDVAV system in WinAM 4.3 with preheat Figure 5-10 Mixed air temperature setpoint (Bes-Tech Inc., and ESL. 2009a) Figure 6-1 IC for electric consumption with the economizer Figure 6-2 IC for chilled water consumption with the economizer Figure 6-3 IC for hot water consumption with the economizer Figure 6-4 IC for electric consumption without the economizer Figure 6-5 IC for chilled water consumption without the economizer Figure 6-6 IC for hot water consumption without the economizer Figure 6-7 Real dollar savings vs. predicted dollar savings without calibration Figure 6-8 Real dollar savings vs. predicted dollar savings with calibration xiv

15 LIST OF TABLES Page Table 3-1 Inputs for baseline model Table 3-2 Adjusted Inputs Table 5-1 Envelope information of Austin City Hall Table 5-2 Air side system information of Austin City Hall Table 5-3 AHU Group 1: AHU 1 and AHU 9 of Austin City Hall Table 5-4 AHU Group 2: AHU 8 and AHU 10 of Austin City Hall Table 5-5 AHU Group 3: AHU 2, 3, 4, 5, 6, and 7 of Austin City Hall Table 5-6 CC measures for Austin City Hall Table 5-7 Calibration for Austin City Hall Table 5-8 Envelope information of DFW Terminal D Table 5-9 Air side system data of DFW Terminal D Table 5-10 AHU Group 1: VAV AHUs of DFW Terminal D Table 5-11 AHU Group 2: SDCAV AHUs of DFW Terminal D Table 5-12 CC measures of DFW Terminal D Table 5-13 Calibration steps for DFW Terminal D Table 5-14 General information for DFW Rent-A-Car Center Table 5-15 CC measures for DFW Rent-A-Car Center Table 5-16 CC measures that cannot be applied to DFW Rent-A-Car Center Table 5-17 Calibration for DFW Rent-A-Car Center Table 5-18 General information of DFW Terminal E Table 5-19 CC measures for DFW Terminal E xv

16 xvi Page Table 5-20 CC measures that cannot be applied to DFW Terminal E Table 5-21 Calibration strategies for DFW Terminal E Table 5-22 Envelope information of Sunset Valley Elementary School Table 5-23 First system information of Sunset Valley Elementary School Table 5-24 General information of Sunset Valley Elementary School Table 5-25 Information for AHU Group 1: DDCVM AHUs of Sunset Valley Elementary School Table 5-26 Information for AHU Group 2: DDCVM AHU (AHU-2) of Sunset Valley Elementary School Table 5-27 Information for AHU Group 3: SDCV of Sunset Valley Elementary School Table 5-28 CC measures for Sunset Valley Elementary School Table 5-29 CC measures that cannot be applied to Sunset Valley Elementary School WinAM 4.3 model Table 5-30 Calibration for Sunset Valley Elementary School Table 5-31 Gas consumption data for Sunset Valley Elementary School Table 5-32 General information of DFW International Airport North Business Tower Table 5-33 CC measures that apply to the information of DFW International Airport North Business Tower Table 5-34 CC measures that cannot be applied to the simulated WinAM 4.3 model of DFW International Airport North Business Tower Table 5-35 Calibrations applied to the DFW International Airport North Business Tower Table 5-36 Envelope information of BACH Table 5-37 AHU Group 1: SDVAV with the economizer of BACH

17 xvii Page Table 5-38 AHU Group 2: SDVAV with the economizer of BACH Table 5-39 AHU Group 3: SDVAV without the economizer of BACH Table 5-40 AHU Group 4: SCVAV with the economizer of BACH Table 5-41 AHU Group 5: SDCAV without the economizer of BACH Table 5-42 AHU Group 6: DDCAV with the economizer of BACH Table 5-43 AHU Group 7: DDCAV without the economizer of BACH Table 5-44 AHU Group 10: Multi-zone constant air volume system with the economizer of BACH Table 5-45 AHU Group 11: Multi-zone constant air volume system without the economizer of BACH Table 5-46 CC measures that apply to BACH Table 5-47 CC measures that cannot be applied to the WinAM 4.3 model Table 5-48 Calibrations for BACH Table 6-1 Summary of IC for each parameter s sensitivity to different energy recourses in the SDVAV system with the economizer Table 6-2 Summary of absolute EPs for each parameter compare with baseline model to different energy recourses in SDVAV system with the economizer Table 6-3 Summary of IC for each parameter s sensitivity to different energy recourses in SDVAV system without the economizer Table 6-4 Summary of absolute EPs for each parameter compared with the baseline model to different energy recourses in SDVAV system without the economizer Table 6-5 Dollar savings for each project Table 7-1 NMBEs and CV(RMSE)s for models without calibration Table 7-2 NMBEs and CV(RMSE)s for models with calibration

18 1. INTRODUCTION The purpose of this research is to analyze the performance of WinAM 4.3. WinAM 4.3 is building performance and energy savings prediction software. It was created by the Continuous Commissioning (CC ) group in the Energy Systems Laboratory at Texas A&M University. The use of WinAM 4.3 helps CC licensees estimate the savings for applying CC measures. WinAM 4.3 can also identify potential energy or cost retrofits and estimate the performance of retrofits (ESL 2012). The approach used in this study will be to perform Sensitivity Analysis (SA) to determine the impact of the major parameters used in WinAM 4.3. In addition, the reliability of the savings estimated will be determined by comparing the calculated savings with the savings reported. The results of the sensitivity analysis task shows the variation in energy consumption that each input parameter has as it is varied over a range of approximately ±3 from the normal value. 14 parameters from WinAM 4.3 software have been chosen for this task, 162 models have be generated after adjusting each parameter within 3 based on base case model. For example, the outside air input variable showed a higher variability than the same percentage of change of Roof U-value for chilled water consumption in a single duct variable air volume system (SD-VAV). In the savings potential reliability analysis, seven CC projects were selected as representative samples to be examined. These include office buildings, a hospital, a kindergarten, and two airport buildings. Calibration results for one of the office 1

19 buildings, Austin City Hall, were also produced using both the WinAM 4.3 and the equest 3-64 (Hirsch et al. 2010) simulation software to see the impact of different simulation engines on the savings results. equest is one of the most popular software packages for analyzing building energy performance (Crawley 2004). This analysis provides a comparison and reference point for the WinAM 4.3 results. Highly sensitive parameters for each energy resource of the systems with and without the economizer are generated in the sensitivity analysis task. The main result of the savings prediction reliability analysis is that calibration improves the model s quality. It also improves the predicted energy savings results compared with the results generated from the uncalibrated models. 2

20 2. LITERATURE REVIEW 2.1 Statistical methods for simulated model calibration ASHRAE Guideline (ASHRAE 2002). The ASHRAE acceptance criteria for calibrated models require the normalized mean bias error (NMBE (%)) to be within ±1 and the coefficient of variation of the root mean square error (CV(RMSE)(%)) to be within ±3 when using hourly data or ±5% and ± 15% when using monthly data, respectively. CV(RMSE) (%) = ( ) ( ) Equation 2.1 NMBE (%) = ( ) ( ) Equation 2.2 = Utility data used for calibration i= Simulation-predicted data = The mean value of the utility data i= Instance p= Sensitivity Analysis (SA) and Uncertainty Analysis (UA) Sensitivity Analysis (SA) In his literature review, Tian (2013) summarized prior work in three areas where sensitivity analysis is important. 3

21 1. By understanding the sensitivity of the inputs, people can understand the impact of saving measures from them and get potential saving (Petr et al. 2007; Lam 2008). 2. Uncertainty and sensitivity analysis coupled with building performance software has the potential to be used for accuracy assessment, design robustness assessment and design guidance (Struck 2009). 3. By detecting the most sensitive input parameter, sensitivity and uncertainty analysis can help the user determine which area of the building needs to be improved (Purdy and Beausoleil-Morrison 2001) Uncertainty Analysis (UA) Although uncertainty analysis and sensitivity analysis look similar, they are different. UA solves for uncertainty in y(x) given the uncertainty in x. SA determines how important the individual elements of x are with respect to the uncertainty in y(x) (Helton 2006). Uncertainty can be separated into three areas (Hopfe et al. 2007; Hopfe and Hensen 2011). These three areas are: 1. Uncertainty in physical parameters: physical uncertainty is relative to the properties of the materials, for example, conductivity, thickness, and density. 2. Uncertainty in design parameters: this uncertainty comes from the planning process, which is completely decided by the decision maker/designer. For 4

22 example, this parameter includes the window s location and all the elements relative to the design of the building. 3. Uncertainty in boundary: this uncertainty parameter includes the unpredicted factors. For example, weather, heat gain from the people inside the building, the natural ventilation controlled by the occupants etc. From ASHRAE Guideline (ASHRAE 2002), the uncertainty in savings can be attributed to errors of assumptions, measurement errors, sampling errors and to errors in the regression model, which include predictive and normalization errors. From UA and SA, we can test the robustness of a model (Litko 2005). We can also learn the most sensitive input parameter, allowing us to avoid errors when simulating the model (Hopfe et al. 2007). Additionally, the use of UA and SA allows us to have a better design for critical issues at the early design stages (Struck and Hensen 2006), e.g. energy consumption, energy cost and thermal comfort (Struck and Hensen 2006) Methodologies for sensitivity analysis The methods for sensitivity analysis for building energy performance can be divided into two categories: global sensitivity analysis and local sensitivity analysis (Tian 2013). Local sensitivity analysis is focused on the difference between the uncertainties caused by one input compared with the base model. In contrast, global sensitivity analysis is focused on the uncertainty caused by all inputs over the whole input space (Tian 2013). 5

23 Differential sensitivity analysis is a form of local sensitivity analysis. Global sensitivity analysis includes both Monte Carlo analysis and stochastic sensitivity analysis. 1. Differential sensitivity analysis (DSA) (Lomas and Eppel 1992) This method adjusts one input and keeps the remaining inputs the same with the baseline for each single simulation. This method has been used repeatedly in the field of building energy analysis (Tian 2013). 2. Monte Carlo analysis (MCA) (Lomas and Eppel 1992; Hopfe et al. 2007) This method adjusts all inputs randomly in each single simulation. A particular distribution will be developed after multiple simulations. 3. Stochastic sensitivity analysis (SSA) (Lomas and Eppel 1992) This method adjusts all inputs simultaneously for each simulation; however, the purpose of SSA is to detect a single parameter s sensitivity Drawbacks of local sensitivity analysis (Tian 2013) Tian found three drawbacks of local sensitivity analysis. They are: 1. Only a limited input factor will be explored around base case. 2. This method cannot detect the interaction between each input factor. 3. There is no self-verification in this method Drawbacks of global sensitivity analysis Since the Monte Carlo analysis requires changes to all the input parameters simultaneously, the sensitivity for each individual input cannot be detected. Like DSA, 6

24 SSA will give the sensitivity for single inputs; however, it requires adjusting all the inputs at the same time. In this way, SSA is different from MCA and DSA, as it requires a complicated calculation (Lomas and Eppel 1992) Steps of Sensitivity Analysis (Tian 2013) Input variations The ranges of the inputs depend on the purpose of the sensitivity analysis. There are three different methods to establish the range of input values in Tian s research. 1. Assess the energy performance in a new building using different design options. This is used for deciding the most energy efficient strategies for the project building. So the range for each input should not be restricted, and allowed to vary over all possible values. 2. Explore the variation of energy use in an existing building. The second range setting method is used for detecting the possible energy consumption variation of the project building and for determining the key variables causing this variation. The setting may also provide an answer for why the measured energy consumption data is different from the simulated energy consumption data for sensitivity analyzing the most sensitive inputs. In this case some of the inputs are fixed, such as the U-value of the wall, roof and windows. The reason for these input shifts may be due to the insulation quality, age of the building, lack of maintenance etc. 7

25 3. Perform the retrofit analysis for an existing building using different energy savings measures. This setting is focused on optimizing the energy consumption by the analysis of different input combinations. For example, use a different insulation thickness and other measures. The two-dimensional Monte Carlo method can be applied to this task, but it will be very complicated Steps to apply the sensitivity analysis experiment After deciding how to choose the range, Tian recommends the following steps for performing the sensitivity analysis: 1. Run building energy models This step is always the most time-consuming part. It requires running simulated models created by building energy model software. The author gives two methods for reducing the simulation time: single computer with multicore or multiprocessor or multiple computers. 2. Adjust the input parameters to get the results This step is used for generating the data obtained from the multiple simulated models for adjusting the different input parameters. 3. Run sensitivity analysis Analyze the inputs and outputs based on the data collected from the step above. 4. Presentation of sensitivity analysis 8

26 The different ways to present sensitivity analysis are: scatter plot, tornado plot, box plot, and spider plot. Among these methods, scatter plot is particularly good at explaining the relationships between inputs and outputs Case study of ten air-conditioned buildings experiment (Lam 2008) The building type for Lam s experiment is an office building. Ten of the key design parameters were chosen to fulfill the sensitivity analysis task. Perturbations were used to assign the range of different values for these 10 inputs. For analyzing the inputs and outputs, the influence coefficient (IC) was applied as the statistic method (Spitler et al. 1989). Equation 2.3 IC = Influence coefficient OP = The output from the adjusted input case; = The output result with the base case input; IP = Adjusted input; = Base case input. The influence coefficient is the ratio of the percentage change in computed output to the percentage change in the input design parameter. 9

27 After calibrating the building through DOE-2 simulation software, only one building in Lam s study does not meet the requirement of ASHRAE error criteria (i.e., 5% or less normal mean bias error and 15% or less root mean square error). 10

28 3. METHODOLOGIES Two types of experiments have been conducted in this research: 1) the sensitivity analysis of input parameters using data produced by WinAM 4.3, and 2) the savings potential reliability analysis based on calibrated WinAM 4.3 models generated from CC project reports. 3.1 Sensitivity analysis of WinAM s input parameters In order to detect the input parameters sensitivity of WinAM 4.3 software, the Differential Sensitivity Analysis (DSA) (Lomas and Eppel 1992) method was applied. DSA involves changing one parameter at a time while keeping the other parameters the same as the baseline. The statistical methods used include the Error Percentage (EP) and the Influence Coefficient (IC) (Spitler et al. 1989; Petr et al. 2007; Lam 2008). The purpose of this research was to identify sensitive parameters. That is parameters where a small change in the input has a large effect on the output. By identifying the sensitive parameters, WinAM users will have a better understanding of where to focus their efforts Create the baseline model The baseline model that will be applied in this research is the square zone model. The building envelope information was taken from BESTEST CASE 600 (Henninger and Witte 2001). BESTEST CASE 600 has numerous detailed settings that cannot be applied to WinAM 4.3. For example, the Solar Heat Gain Coefficient (SHGC) of windows and the thermal mass of the wall are not considered in WinAM

29 Input parameters that have been discussed in this report are : 1) outside air percentage, 2) interior zone percentage, 3) window and wall ratio, 4) minimum airflow ratio, 5) maximum airflow ratio, 6) zone temperature setpoint, 7) cooling coil temperature setpoint, 8) lighting loads, 9) fan power, 10) night plug load, 11) wall R-value, 12) window U-value, 13) roof U-value, and 14) occupancy HVAC system and inputs information The assumption was made that the user will make an error for each input parameter within 3. The baseline model information is given in Table 3-1. Seven models are created for each parameter: 3 model, 2 model, 1 model, 3 model, 2 model, 1 model and the baseline model. For each model, the results for three energy recourse consumptions will be generated: electric energy consumption (this energy consumption only includes lighting, plug loads and fan power), chilled water consumption, and hot water consumption. Monthly and yearly consumption data will be generated separately. Monthly data is used for analyzing the sensitivity under different temperatures. Yearly data can give the parameter s yearly sensitivity. In this research, the SDVAV system with and without the economizer will be studied. The weather data applied to this research is from Austin, Texas. 12

30 Figure 3-1 SDVAV System in WinAM 4.3 Figure 3-1 shows the SDVAV system which has been used in this research. There is no preheating in this system. The supply fan is before the cooling coil and the reheat is configured for each zone. Table 3-1 Inputs for baseline model Parameters Values OA (outside air percentage) 2 IP (interior zone percentage) 6 W-W (window-wall ratio) 3 Min (minimum airflow rate) 0.3 Max (maximum airflow rate) 1 Tz (zone temperature) 70 Tc (cooling coil temperature) 50 Lighting (average lighting energy consumption) 1 Plug (average plug energy consumption) 1 Occ (peak occupancy) 150 FP (VAV fan power) 1 13

31 Table 3-1 continued Parameters Values NLPL (nighttime lighting and plug load ratio) 0.2 Wall R (exterior wall R-value) 12 Window U (exterior window U-value) 0.75 Roof U (roof U-value) Each parameter was adjusted to 1, 2 and 3 compared with the parameter in the baseline model. The adjusted parameters are shown in Table 3-2. Due to zone temperatures of 49 F (-3 compared with baseline model), 56 F (-20 compared with baseline model), and 91 F (-3 compared with baseline model) being outside of the acceptable input range setting in WinAM 4.3, these inputs will not be discussed here. This is the same reason for some of the inputs were not discussed for cooling coil temperature setpoint. Table 3-2 Adjusted Inputs Magnitude OA (outside air percentage) IP (interior zone percentage) W-W (window-wall ratio) Min (minimum airflow rate) Max (maximum airflow rate) Tz (zone temperature) Tc (cooling coil temperature)

32 Table 3-2 continued Magnitude Lighting (average lighting energy consumption) FP (VAV fan power) NLPL (nighttime lighting and plug load ratio) Wall R (exterior wall R-value) Window U (exterior window U-value) Roof U (roof U-value) Occ (peak occupancy) Statistical methods used for analyzing results The analysis of the results has been divided into three parts. 1. Adjusted inputs impacts analysis. We analyzed monthly electric consumption, chilled water consumption and hot water consumption according to adjusted input parameters based on average monthly temperature. The purpose is to learn WinAM 4.3 s input properties. Error Percentage (EP) has been used as the statistical method to assist in analyzing the results. The EP is the error of the adjusted case with respect to the base case. 15

33 Equation 3.1 = energy consumption data for base case; = energy consumption data for adjusted case. 2. Ranking the sensitivity of inputs based on warm and cold temperatures. The assumption is made that outside air temperature lower than 50 will be defined as a cold temperature, and outside air temperature higher than 70 is a hot temperature. Influence Coefficient (IC) will be used as the statistical method to analyze the results. The IC is the ratio of the percentage change in computed output to the percentage change in the input design parameter (Spitler et al. 1989). The Influence Coefficient was defined in Equation Input parameters sensitivity ranking based on the whole year energy consumption. The Influence Coefficient (IC) statistical method was used to analysis the results in this step. This step differs from the previous step by using yearly data instead of monthly data. 3.2 Predicted savings potential reliability analysis The predicted savings reliability analysis performed here compares the saving predicted by the simulated model generated from WinAM 4.3 CC projects with the measured saving from CC project reports. 16

34 3.2.1 Steps for establishing experiment Figure 3-2 is the flowchart for the method used for the saving prediction task. Model a.*(* denote 1, 2, 3 ) is the un-calibrated model, model b.* is the calibrated model. Figure 3-2 Flowchart of savings prediction Select suitable experiment subjects according to CC reports 11 potential candidate CC building projects were chosen to be experiment subjects. They are: 17

35 Austin City Hall (ACH) (Zhou et. al 2009) Bayne-Jones Army Community Hospital (BJACH) (Bes-Tech Inc. and ESL 2009a) Blanchfield Army Community Hospital (BACH) (Bes-Tech Inc. and ESL 2009b) Fox Army Health Center (FAHC) (HHS Associates LLC and ESL 2009b) Martin Army Community Hospital ( MACH) (Effinger et al. 2008) North Business Tower of DFW International Airport (ESL 2010b) Rent-A-Car Center of DFW International Airport (Zeig et al. 2004) Sunset Valley Elementary School (SVES) (Yagua et al. 2009) Tripler Army Medical Center (TAMC) (HHS Associates LLC and ESL 2009a) Terminal D of DFW International Airport (ESL 2010a) Terminal E of DFW International Airport (ESL 2010c) Included in the list are five hospitals, two airports, one city hall, one garage building, one office building and one K-12 building. To be qualified as an experiment subject, the projects above needed to document the following information: System change for pre and post CC measurements. Basic building and HVAC system information Take the Continuous Commissioning Final Report for Bayne-Jones Army Community Hospital ( BJACH) 2009 as an example. This report documents the 18

36 minimum airflow rate were set at 5 of maximum flow pre-cc control, but there is no maximum airflow rate data This report also lists four types of AHU systems but fails to give further information on the conditioned area of each AHU system. After checking the CC report for each project based on the criteria, seven projects were selected. Austin City Hall (ACH) Blanchfield Army Community Hospital (BACH) North Business Tower of DFW International Airport Rent-A-Car Center of DFW International Airport Sunset Valley Elementary School (SVES) Terminal D of DFW International Airport Terminal E of DFW International Airport Create baseline model The baseline model will be created based on the information documented in the Continuous Commissioning reports. The missing information required in inputs of WinAM 4.3 will be decided after a discussion with the engineer who performed or was familiar with the CC project (the seven CC projects mentioned in ). If the information cannot be obtained from any recode, the input will be decided based on the WinAM 4.3 help manual: how to use WinAM to calculate savings from energy conservation measures (ESL 2013a). 19

37 Obtain the weather data The weather data will be generated from the CC -Compass website created by the Energy Systems Laboratory (ESL. 2013b). Figure 3-3 Weather data tool from CC -Compass (ESL. 2013b) The steps for using the on-line weather tool shown in Figure 3-3 are as follows: 1. Enter search terms, select a mile radius if necessary, and press search. 2. If the weather station needed does not appear on the list, try expanding the search area. 3. Click the readings button to view readings, and the export button to generate a weather file. 20

38 For most models, the weather data will be obtained by taking above steps. Austin City Hall requires extra work to obtain its weather data, which is documented in Appendix A. For testing the difference between WinAM4.3 and the similar building performance software, equest 3.64 has been selected. equest 3.64 uses typical meteorological year (TMY) weather data. It is not the real weather data for each year but the typical temperature to represent the weather phenomena for the certain location. Appendix A gives a detailed method on how to obtain the weather data. This method was used to obtain weather information for Austin City Hall Model simulation 1. Model a.1, simulated base model without calibration. The inputs for this model are obtained from the Continuous Commissioning report. Some of the data not documented in the report came from the engineer who performed the CC report or is imputed using the project average value. 2. Model b.1, calibrated model based on the real measured data. After creating Model a.1, the measured data was input into WinAM 4.3. With the help of the calibration assistant, the model was calibrated to the minimum error Apply CC measures to the simulated model 1. Model a.2, CC measures for model without calibration. Model a.2 is the model with CC measures based on Model a.1. CC measures applied in this step are obtained from the CC reports. 21

39 2. Model b.2, CC measures for model with calibration. Model b.2 is the model with CC measures based on Model b.1. CC measures applied in this step are obtained from the CC reports Calculate savings 1. Calculate the predicted savings from Model a.1 and Model a.2. Calculate the energy savings percentage by using energy consumptions from Model a.1and the energy consumptions from Model a.2. The result should be calculated in dollars. 2. Calculate the predicted savings from Model b.1 and Model b.2. The method for this step is the same as the method to calculate the predicted savings from Model a.1 and Model a.2, the only difference is Model b.1 and b.2 were used instead of Model a.1 and a.2. 22

40 4. INPUTS PROPERTIES ANALYSIS This section focuses on the inputs analysis. Equation 4-1 to Equation 4-6 are important in understanding the physics of the phenomenon caused by adjusting the inputs. Equation 4-1 Equation 4-2 Equation 4-3 Equation 4-4 Equation 4-5 Equation 4-6 The cooling load on cooling coil, Btu/hr; Heat factor, ; Difference between the supply air temperature and the mixture air temperature, F; Fan s full load power; Fan s real power; The fraction of the fan s full load power; The fraction of actual flow rate to maximum flow rate; The actual flow rate, ; The maximum flow rate, ; 23

41 Temperature across the fan, F; Specific fan power, kw/cfm; Density of the air, lbm/ft 3 Specific heat of the air, ; Converts to Kw; Heat transfer rate, Btu/hr; Temperature difference between outside air temperature and zone temperature, ; Resistance of heat transfer,. 4.1 Outside air percentage (OA) The baseline for outside air percentage is 2. Then, the baseline percentage needs to be adjusted from -3 to +3. Thus, the inputs for the outside air percentage are: 14%, 16%, 18%, 2, 22%, 24% and 26%. This is reflected in the legends in Figure 4-1 and Figure 4-2. The other charts in this section are presented in a similar way, with adjustment from to from the baseline value. Seven models will be created for analyzing the behavior of the outside air percentage parameter. In WinAM 4.3, this function has been named minimum OA percentage. Although it has been named as minimum, it will be a constant value in the system without the economizer. The influence coefficients in Section 4 are calculated based on monthly data. After calculating the average influence coefficients, all these results will be reassigned based on the monthly average temperature from low to high. 24

42 Error percentage for chilled water Electricity The electricity in this section only includes the fan power and the lighting/plug loads. If the lighting/plug loads do not change, fan power is the only variable to decide whether the electricity will be changed or not. For the VAV system, fan power will be affected by the cooling and heating loads. When adjusting the outside air percentage from 14% to 26%, no change occurred in electricity. This means when adjusting outside air percentage within 3 based on the original setting are not causing the fan to work more or work less in this model Chilled water Outside air percentage 1 8% 6% 4% 2% -2% % -6% -8% -1 Outside air temperature F 26% 24% 22% 2 18% 16% 14% Figure 4-1 Chilled water consumption for the OA% parameter, SDVAV with the economizer 25

43 Error percentage for chilled water Figure 4-1 shows the chilled water consumption difference percentage of the SDVAV system with the economizer. The temperature economizer range in this experiment is from 30 F to 60 F. If the system has the economizer, when outside air temperature (OAT) is between 30 F and 60 F, free cooling will be used. The chart implies that when increasing the outside air percentage, the chilled water consumption will be increased. This effect is more obvious when the outside air temperature is high. Under hot OATs, when the outside air volume percentage is increased, the mixed air temperature will be increased. From Equation 4-1, when the fan power does not change, more chilled water will be required to meet the cooling requirement from the cooling coil. Outside air percentage 1 8% 6% 4% 2% -2% % -6% -8% -1 Outside air temperature F 14% 16% 18% 2 22% 24% 26% Figure 4-2 Chilled water consumption for the OA% parameter, SDVAV without the economizer 26

44 Influence coefficient Figure 4-2 shows the chilled water consumption difference percentage of the SDVAV system without the economizer. The chilled water consumption will be decreased when increasing the OA percentage in cold OATs. This is because the minimum OA percentage keeps the OA percentage at input value. So even in the cold OATs the system does not require that much outside air. OA% chilled water IC vs. OAT W-IC 4% 5% 4% 7% 1 19% 21% 23% 26% 28% 28% W/O-IC -28% -17% -5% -5% -3% 6% 15% 2 23% 26% 28% 28% Figure 4-3 Chilled water consumption IC value for the parameter OA% Figure 4-3 shows the influence coefficient for both systems with and without the economizer. economizer, and represents the influence coefficient of the system with the represents the influence coefficient of the system without the economizer. This figure shows that the SDVAV system without the economizer is more sensitive when the monthly average outside air temperature (OAT) is below 61.6 F. The influence coefficients should be equal between both systems when the 27

45 average temperature is higher than 60 F, because the free cooling temperature range for the economizer is between 30 F to 60 F. The reason for the difference is that the temperature used here is the monthly average temperature. That means although the mean temperature is higher than 60 F (the economizer should not be operated), the daily temperatures in this month can be lower than 60 F which means the economizer still work sometimes in this month even if the average monthly OAT is higher than 60 F Hot water This experiment shows that the hot water will not be impacted by adjusting the outside air percentage parameter for the SDVAV system with or without the economizer. This is because the required supply airflow rate does not change, so the supply air from the cooling coil is always at 55 F. Consequently the hot water consumption is always the same. 4.2 Interior zone percentage (IP) Figure 4-4 shows how WinAM 4.3 assigns the interior zone and the exterior zone. The darker color represents the exterior zone and the lighter color represents the interior zone. 28

46 Figure 4-4 Interior zone and exterior zone Loads of the interior zone are only affected by lighting/plug and occupancy loads in WinAM 4.3. In WinAM 4.3, loads in the exterior zone will also be affected by outside air temperature. The results for electric consumption, chilled water consumption and hot water consumption are the same for the SDVAV system with and without the economizer. 29

47 Error percentage for electric Electricity Interior zone percentage 4% 3% 2% 1% -1% % -3% -4% -5% Outside air temperature F Figure 4-5 Electric consumption for the parameter IP, SDVAV without the economizer When the interior zone percentage is reduced, electric consumption will be increased, Figure 4-5. That means the fan needs to do more work to keep the zone temperature at its setpoint. As discussed before, the reason for that is because more area will be affected by outside air temperature. The effect on electricity caused by the interior percentage parameter is more sensitive in hot OATs. The electric consumption difference percentage is the same for the system with and without the economizer. This can be proved by the influence coefficients from both systems shown in Figure

48 Error percentage for chilled water Influence coefficient IP - electric IC vs. OAT 2% -2% -4% -6% -8% -1-12% -14% W-IC -1% -4% -4% -11% -11% -12% W/O-IC -1% -4% -4% -11% -11% -12% Figure 4-6 Electric consumption IC value for the parameter IP Chilled water Interior zone percentage 6% 4% 2% -2% % -6% -8% -1-12% -14% Outside air temperature F 42% 48% 54% 6 66% 72% 78% Figure 4-7 Chilled water consumption for the parameter IP when SDVAV without the economizer 31

49 Influence coefficient The patterns of error percentage results of chilled water consumption are shown in Figure 4-7. When the interior zone percentage is increased, less chilled water will be consumed. This is because the interior zone load isn t impacted by outside air temperature, so the chilled water consumption for the interior zone mainly depends on the temperature across the fan, the lighting plug loads and the occupancy. Under this condition, the larger the exterior zone is the more area will be affected by outside air temperature. The chilled water consumption influence coefficients in Figure 4-8 shows that the effect of the interior percentage parameter is almost the same for both the system with and the system without the economizer. IP - chilled water IC vs. OAT 1 5% -5% -1-15% -2-25% W-IC 5% 4% 3% 3% 3% 2% -1% -8% -7% -24% -23% -26% W/O-IC 4% 4% 3% 3% 3% 2% -1% -7% -7% -24% -23% -26% Figure 4-8 Chilled water consumption IC value for the parameter IP 32

50 Error percentage for hot water Hot water Interior zone percentage Outside air temperature F 78% 72% 66% 6 54% 48% 42% Figure 4-9 Hot water consumption for the parameter IP, SDVAV without the economizer For the SDVAV system with and without the economizer, Figure 4-9 shows that when the interior percentage is reduced, the hot water consumption will be reduced. This is more sensitive in hot OATs. 33

51 Influence coefficient IP - hot water IC vs. OAT W-IC 2% 3% 5% 4% 6% 11% 16% 46% 54% 87% 98% 99% W/O-IC 2% 3% 5% 4% 6% 11% 16% 46% 54% 87% 98% 99% Figure 4-10 Hot water consumption IC value for the parameter IP Figure 4-10 shows that the influence coefficients are the same for both systems, and it is more sensitivity in the hot weather. 4.3 Window-wall ratio (W-W) In WinAM 4.3, the window only has a U-value. Adjusting the window-wall ratio here will only result in changing the average U-value of the wall. A similar situation will happen to the window U-value parameter also. 34

52 Error percentage for electric Electricity 2% Window-wall ratio 1% 1% % -1% -2% Outside air temperature F 39% 36% 33% 3 27% 24% 21% Figure 4-11 Electric consumption for the parameter W-W, SDVAV with the economizer Figure 4-11 shows that when the window-wall ratio is reduced, the electric consumption usage will be reduced too. This parameter is more sensitive in the hot OATs for electric consumption and it can be explained as the U-value of the wall will be reduced or increased if enlarging or decreasing the area of the window. When the U-value of the wall is increased, the insulation of the wall is reduced at the same time. A wall with a higher U-value is not as good as a wall with a lower U-value in keeping the building temperature constant. In this condition, the fan needs to do more work to keep the zone temperature at its setpoint. 35

53 Influence coefficient W-W - electric IC vs. OAT 4% 4% 3% 3% 2% 2% 1% 1% W-IC 1% 1% 1% 1% 2% 3% 3% 3% 4% 4% W/O-IC 1% 1% 1% 1% 2% 3% 3% 3% 4% 4% Figure 4-12 Electric consumption IC value for the parameter W-W Figure 4-12 shows the window-wall influence coefficients are the same for the system with and without the economizer, and it is more sensitive for electric consumption in the hot OAT. 36

54 Error percentage for chilled water Chilled water 6% Window-wall ratio 4% 2% % % -6% -8% Outside air temperature F 39% 36% 33% 3 27% 24% 21% Figure 4-13 Chilled water consumption for the parameter W-W, SDVAV with the economizer The reason for the result of chilled water consumption difference percentage shown in Figure 4-13 is that the zone needs to be cooled throughout the year. The physics are the same as for electric consumption. 37

55 Influence coefficient 18% 16% 14% 12% 1 8% 6% 4% 2% W-W - chilled water IC vs. OAT W-IC 1% 4% 5% 5% 8% 9% 12% 15% 15% 16% 17% 17% W/O-IC 2% 4% 4% 6% 8% 11% 15% 15% 16% 17% 17% Figure 4-14 Chilled water consumption IC value for the parameter W-W The window-wall ratio parameter is sensitive to the system with the economizer under the cold OATs, Figure Because of the free cooling, the economizer uses less chilled water. 38

56 Error percentage for hot water Hot water Window-wall ratio 2 15% 1 5% % % -2 Outside air temperature F 39% 36% 33% 3 27% 24% 21% Figure 4-15 Hot water consumption for the parameter W-W SDVAV with the economizer When reducing the window-wall ratio, the hot water consumption is reduced, as shown in Figure This phenomenon is easy to explain. The thermal resistance ability has been reduced, so the project zone s ability for keeping warm has been reduced, which means the system needs more hot water to keep the zone warm. 39

57 Influence coefficient W-W - hot water IC vs. OAT W-IC 55% 53% 46% 45% 46% 35% 36% 7% 4% 1% -1% W/O-IC 55% 53% 46% 45% 46% 35% 36% 7% 4% 1% -1% Figure 4-16 Hot water consumption IC value for the parameter W-W Figure 4-16 shows that the hot water influence coefficients are higher in cold weather than hot weather which is caused by the window-wall ratio parameter. This means that the hot water consumption is more sensitive to the window-wall ratio parameter in cold weather. 4.4 Minimum airflow rate (Min) The units for minimum airflow rate parameter in this research is cubic feet per minute per square foot. 40

58 Error percentage for chilled water Electricity 6% Exterior window U-value 4% 2% % % -6% -8% Outside air temperature F (Btu/sf) *hr* 0.9 (Btu/sf) *hr* (Btu/sf) *hr* 0.75 (Btu/sf) *hr* (Btu/sf) *hr* 0.6 (Btu/sf) *hr* (Btu/sf) *hr* Figure 4-17 Electric consumption for the parameter Min, SDVAV without the economizer Figure 4-17 shows that when the minimum airflow rate is reduced, the electric consumption will be reduced. And electric consumption is more sensitive to this parameter in cold weather. An example follows to explain this phenomenon. The minimum airflow rate is 0.3 /ft 2 for the base case, but the zone only requires a 0.1 /ft 2 minimum flow rate. If the engineer reduces the minimum airflow rate from 0.3 /ft 2 to 0.2 /ft 2, although 0.2 /ft 2 is not the perfect minimum airflow rate, the fan requires less power than at the 0.3 CFM/ft 2 setting. 41

59 Influence coefficient 9% 8% 7% 6% 5% 4% 3% 2% 1% Min - electric IC vs. OAT W-IC 8% 8% 7% 7% 7% 6% 6% 5% 5% 5% 5% 5% W/O-IC 8% 8% 7% 7% 7% 6% 6% 5% 5% 5% 5% 5% Figure 4-18 Electric consumption IC value for the parameter Min Figure 4-18 shows that the sensitivity of the minimum airflow rate parameter is the same for both the SDVAV system with the economizer and without the economizer. 42

60 Error percentage for chilled water Chilled water Minimum airflow rate 3 25% 2 15% 1 5% -5% % -2-25% Outside air temperature F 0.39CFM/sf 0.36CFM/sf 0.33 CFM/sf 0.3 CFM/sf 0.27CFM/sf 0.24 CFM/sf 0.21 CFM/sf Figure 4-19 Chilled water consumption for the parameter Min, SDVAV without the economizer Figure 4-19 implies that the chilled water consumption difference percentage will be reduced when the minimum airflow rate is decreased. The reason is similar to the electric consumption. When decreasing the minimum airflow rate, the system can save energy when it does not need to offer that much cooling. 43

61 Influence coefficient Min - chilled water IC vs. OAT W-IC 64% 5 45% 45% 41% 39% 32% 24% 23% 16% 15% 15% W/O-IC 7 63% 52% 51% 47% 41% 35% 24% 23% 16% 15% 15% Figure 4-20 Chilled water consumption IC value for the parameter Min Figure 4-20 shows that in cold OATs the system with the economizer is less sensitive to the minimum airflow rate parameter than the system without the economizer. This is because the system with the economizer can get free cooling from the outside air. In this way, the chilled water consumption will not be changed as much as its consumption for the system without the economizer. 44

62 Influence coefficient Error percentage for hot water Hot water Minimum airflow rate Outside air temperature F 0.39CFM/sf 0.36CFM/sf 0.33 CFM/sf 0.3 CFM/sf 0.27CFM/sf 0.24 CFM/sf 0.21 CFM/sf Figure 4-21 Hot water consumption for the parameter Min, SDVAV without the economizer Min - hot water IC vs. OAT W-IC 37% 42% 6 61% 59% 86% 85% 139% 144% 162% 162% 166% W/O-IC 37% 42% 6 61% 59% 86% 85% 139% 144% 162% 162% 166% Figure 4-22 Hot water consumption IC value for the parameter Min 45

63 Error percentage for electric Figure 4-21 and Figure 4-22 show the impact of hot water consumption caused by adjusting the minimum airflow rate parameter. This impact is opposite that of electric and chilled water consumption. The reason is that in cold OATs the hot water will be consumed more to balance the heat loss in the cold weather. Under this condition, for example, system A needs to pump the same amount of extra hot water when increasing the minimum air volume to keep the zone temperature at its setpoint. The minimum airflow rate for system A is 0.3. In the cold weather, system A needs 3 of hot water to keep the zone temperature at its setpoint, and needs 2 of hot water in hot weather. 4.5 Maximum airflow rate (Max) Electric 4% Maximum airflow rate 3% 2% 1% % % -3% Outside air temperature F 0.7 cfm/sf 0.8 cfm/sf 0.9 cfm/sf 1 cfm/sf 1.1 cfm/sf 1.2 cfm/sf 1.3 cfm/sf Figure 4-23 Electric consumption for the parameter Max, SDVAV with the economizer 46

64 Influence coefficient Max - electric IC vs. OAT -1% -2% -3% -4% -5% -6% -7% -8% W-IC -6% -6% -7% -7% -7% -8% -7% -7% -7% -3% -3% -3% W/O-IC -6% -6% -7% -7% -7% -8% -7% -7% -7% -3% -3% -3% Figure 4-24 Electric consumption IC value for the parameter Max Figure 4-23 and Figure 4-24 show the maximum airflow rate parameter for electric consumption is more sensitive in the range of average monthly OAT: 47 F to 78 F. This parameter effect is the same for the systems with and without the economizer. The electric consumption is increased when the maximum airflow rate is decreased. The reason for this can be explained with the following example. Suppose the requirement for maintaining the zone temperature at its setpoint when the outside air temperature is 80 F is that the maximum airflow rate for the system is 1CFM/ft 2. When the maximum airflow rate is decreased, the zone temperature cannot be kept at its setpoint. That is why when the maximum airflow rate decreases, the electric consumption will increase. 47

65 Influence coefficient Error percentage for chilled water Chilled water Maximum airflow rate 8% 6% 4% 2% -2% % -6% -8% -1-12% -14% Outside air temperature F 0.7 cfm/sf 0.8 cfm/sf 0.9 cfm/sf 1 cfm/sf 1.1 cfm/sf 1.2 cfm/sf 1.3 cfm/sf Figure 4-25 Chilled water consumption for the parameter Max, SDVAV with the economizer 35% 3 25% 2 15% 1 5% -5% -1-15% Max - chilled water IC vs. OAT W-IC -8% -4% -3% -4% -1% 6% 12% 12% 27% 27% 29% W/O-IC -5% -4% -2% -4% -2% 5% 12% 12% 27% 27% 29% Figure 4-26 Chilled water consumption IC value for the parameter Max 48

66 Figure 4-25 shows when the average monthly OAT is below 61.6 F (include 61.6 F). The maximum airflow rate parameter effect of chilled water consumption for both systems with and without the economizer is the same in hot weather, while the effect is more sensitive in cold weather for the system with the economizer, Figure Decreasing the maximum airflow rate, the chilled water consumption will increase. When the outside air temperature is higher than 61.6 F, decreasing the maximum airflow rate will cause the chilled water consumption to decrease. The reason for that can be explained by the fan model used in WinAM 4.3. When reducing the maximum flow rate, the fraction of the fan s full load power will be increased if the required flow rate does not change. Under this condition, the fan power will be increased. When the fan power is increased the temperature across the fan will also be increased. More chilled water is required to cool down the increased temperature that is caused by the fan. This is the reason that when the maximum airflow rate is decreased the chilled water consumption will be increased. 49

67 Error percentage for hot water Hot water Maximum airflow rate Outside air temperature F 1.3 CFM/sf 1.2 CFM/sf 1.1 CFM/sf 1 CFM/sf 0.9 CFM/sf 0.8 CFM/sf 0.7 CFM/sf Figure 4-27 Hot water consumption for the parameter Max, SDVAV with the economizer Figure 4-27 shows that the maximum airflow rate influence coefficients for hot water consumption are almost zero for the system with the economizer and the system without the economizer. Therefore, the effect of this parameter on hot water consumption can be ignored. 50

68 Error percentage for electric 4.6 Zone temperature (Tz) Electric 15% Zone temperature 1 5% % F 70 F 77 F 84 F -15% Outside air temperature F Figure 4-28 Electric consumption for the parameter Tz, SDVAV with the economizer Figure 4-28 shows that the electric consumption will be increased if the zone temperature setpoint decreases. The cooling coil temperature will not be changed, so for achieving the lower zone temperature setpoint, the fan needs to work harder. The peak of the curve in Figure 4-28 means the fan is at its maximum speed. Under this condition when OAT keeps increasing, the fan will keep constant speed. 51

69 Influence coefficient Tz - electric IC vs. OAT W-IC -14% -24% -37% -36% -41% -52% -54% -74% -74% -72% -73% -72% W/O-IC -14% -24% -37% -36% -41% -52% -54% -74% -74% -72% -73% -72% Figure 4-29 Electric consumption IC value for the parameter Tz Figure 4-29 implies that the zone temperature setpoint parameter has the same sensitivity for both the SDVAV system with the economizer and system without the economizer. Additionally, the zone temperature setpoint parameter is more sensitive to electric consumption in hot OATs. 52

70 Error percentage for chilled water Error percentage for chilled water Chilled water Zone temperature F 70 F 77 F 84 F -3 Outside air temperature F Figure 4-30 Chilled water consumption for the parameter Tz, SDVAV with the economizer Zone temperature Outside air temperature F 63 F 70 F 77 F 84 F Figure 4-31 Chilled water consumption for the parameter Tz, SDVAV without the economizer 53

71 Influence coefficient Figure 4-30 and Figure 4-31 are the chilled water consumption difference percentage data for the system with the economizer and the system without the economizer. For the system with the economizer, the chilled water consumption will increase when decreasing the zone temperature in cold weather, while the chilled water consumption for the system without the economizer will decrease in the cold weather. For both systems, the chilled water consumption will decrease when the average monthly OAT is higher than 61.6 F. This phenomenon can be explained using Equation 4-1. The for the system with the economizer will not be impacted as much as for the system without the economizer. In the system with the economizer, the outside air temperature can be used for balancing this extra heat gain from the zone Tz - chilled water IC vs. OAT W-IC -9% -36% -55% -44% -77% -78% -118% -143% -146% -132% -139% -133% W/O-IC 237% 149% 53% 55% 25% -4-73% % -132% -139% -133% Figure 4-32 Chilled water consumption IC value for the parameter Tz 54

72 Error percentage for hot water Figure 4-32 shows the chilled water influence coefficient caused by the zone temperature. The chilled water consumption for the system without the economizer is more sensitive than the system with the economizer in cold OATs. In the hot OATs the sensitivity for both systems is almost the same Hot water 50 Zone temperature Outside air temperature F 84 F 77 F 70 F 63 F Figure 4-33 Hot water consumption for the parameter Tz, SDVAV with the economizer 55

73 Influence coefficient Tz - hot water IC vs. OAT W-IC 404% 446% 653% 679% 646% 932% 906% 1413% 1504% 1255% % W/O-IC 409% 455% 655% 679% 648% 932% 906% 1413% 1504% 1255% % Figure 4-34 Hot water consumption IC value for the parameter Tz Figure 4-33 shows that when the zone temperature increases, the hot water consumption will increase simultaneously. This is because when increasing the zone temperature setpoint, more hot water is expected to be consumed to meet the setpoint. Figure 4-34 shows that the influence coefficient value has been reduced for temperatures over F. When the monthly average OAT is higher than F, the zone absorbs the heat from the outside air for most of the time in that month, so the hot water consumption will be decreased. 56

74 Influence coefficient Error percentage for electric 4.7 Cooling coil temperature (Tc) Electric 3 Cooling coil temperature 25% 2 15% 1 5% % Outside air temperature F 65 F 60 F 55 F 50 F 45 F 40 F Figure 4-35 Electric consumption for the parameter Tc, SDVAV with the economizer Tc - electric IC vs. OAT W-IC 404% 446% 653% 679% 646% 932% 906% 1413% 1504% 1255% % W/O-IC 409% 455% 655% 679% 648% 932% 906% 1413% 1504% 1255% % Figure 4-36 Electric consumption IC value for the parameter Tc 57

75 Error percentage for chilled water Figure 4-35 shows that when increasing the cooling coil temperature, the electric consumption will increase. This is because the fan needs to work harder to blow more air through the coil to balance the cooling load in the zone (Equation 4-1). When the fan works at its maximum load under a certain temperature, the variable-frequency drive fan will work as the constant speed fan. That is why even though the outside air temperature gets warmer, the sensitivity caused by the cooling coil temperature to electric consumption is reduced, as shown in Figure Figure 4-36 shows that the electric consumption influence coefficients, which are caused by adjusting the cooling coil temperature, are the same for both the system with the economizer and the system without the economizer Chilled water 25 Cooling coil temperature F 45 F 50 F 55 F 60 F 65 F -10 Outside air temperature F Figure 4-37 Chilled water consumption for the parameter Tc, SDVAV with the economizer 58

76 Error percentage for chilled water Cooling coil temperature Outside air temperature F 40 F 45 F 50 F 55 F 60 F 65 F Figure 4-38 Chilled water consumption for the parameter Tc, SDVAV without the economizer Figure 4-37 and Figure 4-38 show the chilled water consumption for the SDVAV system with the economizer and the system without the economizer, respectively. The patterns of the chilled water consumption for these two systems are similar to each other. The chilled water consumption is increased by reducing the cooling coil temperature. This can be explained by Equation 4-1. The will be increased when reducing cooling coil temperature, so if the reduction in flow rate is not significant, the cooling load on the cooling coil will be increased. As shown in Figure 4-39, the sensitivity caused by cooling coil temperature adjustments to the chilled water consumption are also similar for these systems, except for a monthly average outside air temperature below 47 F. The chilled water consumption for the 59

77 Influence coefficient system with the economizer is more sensitive than the system without the economizer in cold OATs. Tc - chilled water IC vs. OAT W-IC -541% -222% -185% -204% -173% -127% -114% -76% -66% -69% -62% -63% W/O-IC -254% -209% -161% -166% -155% % -75% -66% -69% -62% -63% Figure 4-39 Chilled water consumption IC value for the parameter Tc 60

78 Influence coefficient Error percentage for hot water Hot water Cooling coil temperature F 45 F 50 F 55 F 60 F 65 F -15 Outside air temperature F Figure 4-40 Hot water consumption for the parameter Tc, SDVAV with the economizer Tc - hot water IC vs. OAT W-IC -63% -73% -136% -146% -131% % -353% -363% -403% -404% -409% W/O-IC -96% -107% -153% -158% -152% -218% -216% -353% -363% -403% -404% -409% Figure 4-41 Hot water consumption IC value for the parameter Tc 61

79 Error percentage for electric Figure 4-40 shows the hot water consumption results for the systems with the economizer and without the economizer. When the outside air temperature is between the economizer enabled ranges of 30 F to 60 F, the results for these two systems are different from each other, otherwise they are the same, see Figure The reason for this is that free cooling in the economizer can adjust the mixed temperature to the best fit for the zone temperature by adjusting the cooling coil temperature. That makes the hot water consumption less sensitive than in the system without the economizer. 4.8 Average lighting energy consumption (Lighting) Electric 2 15% 1 5% % % -2 Average lighting energy consumption Outside air temperature F 1.3 W/sf 1.2 W/sf 1.1 W/sf 1 W/sf 0.9 W/sf 0.8 W/sf 0.7 W/sf Figure 4-42 Electric consumption for the parameter lighting, SDVAV with the economizer 62

80 Influence coefficient Figure 4-42 shows that the electric consumption is decreased by decreasing the lighting power. This can be explained by Equation 4-1. When the zone cooling load decreases, the coil cooling load is reduced, the temperature difference between mixed air and supply air does not change, and airflow is reduced for keeping the zone temperature at its setpoint. From Equation 4-2 through Equation 4-4, less airflow leads to lower fan power. The physical explanation is the same as the relationship between the plug load parameter and the nighttime lighting and plug load ratio. Lighting - electric IC vs. OAT 5 49% 48% 47% 46% 45% 44% 43% W-IC 49% 49% 49% 49% 49% 49% 48% 48% 48% 46% 45% 45% W/O-IC 49% 49% 49% 49% 49% 49% 48% 48% 48% 46% 45% 45% Figure 4-43 Electric consumption IC value for the parameter lighting Figure 4-43 shows that the sensitivity of electric consumption caused by the lighting parameter is the same for both the systems with the economizer and without the economizer. 63

81 Error percentage for chilled water Chilled water 8% 6% 4% 2% % % -6% -8% Average lighting energy consumption Outside air temperature F 1.3 W/sf 1.2 W/sf 1.1 W/sf 1 W/sf 0.9 W/sf 0.8 W/sf 0.7 W/sf Figure 4-44 Chilled water consumption for the parameter lighting, SDVAV with the economizer The pattern of the results for chilled water consumption is the same for both SDVAV systems with and without the economizer. When the lighting power has been reduced, the chilled water consumption will be reduced, see Figure The reason for this is that the cooling load in the zone will be reduced, so the requirement for chilled water will be reduced. 64

82 Influence coefficient 2 18% 16% 14% 12% 1 8% 6% 4% 2% Lighting - chilled water IC vs. OAT W-IC 17% 18% 18% 19% 18% 17% 16% 15% 15% 12% 12% 11% W/O-IC 14% 16% 17% 17% 17% 17% 16% 15% 15% 12% 12% 11% Figure 4-45 Chilled water consumption IC value for the parameter lighting Figure 4-45 shows that the system with the economizer is more sensitive in cold OATs. The reason is similar to the window-wall ratio parameter effect for chilled water. 65

83 Error percentage for hot water Hot water Average lighting energy consumption 8% 6% 0.7 W/sf 4% 0.8 W/sf 2% 0.9 W/sf 1 W/sf % 1.1 W/sf -4% 1.2 W/sf -6% 1.3 W/sf -8% Outside air temperature F Figure 4-46 Hot water consumption for the parameter Tz, SDVAV with the economizer Figure 4-46 shows that hot water consumption will be increased when the lighting power is decreased. Lighting is the main electric equipment for offering heat gain. 66

84 Influence coefficient -2% -4% -6% -8% -1-12% -14% -16% -18% -2 Lighting - hot water IC vs. OAT W-IC -8% -9% -12% -12% -11% -15% -15% -18% -18% -18% -17% -17% W/O-IC -8% -9% -12% -12% -11% -15% -15% -18% -18% -18% -17% -17% Figure 4-47 Hot water consumption IC value for the parameter lighting Figure 4-47 shows that the hot water consumption caused by adjusting the lighting parameter is more sensitive in warm OATs. This is because in warm weather the hot water consumption is less than that in cold weather. Assume the total hot water consumption is the denominator. The change in the hot water consumption caused by adjusting the lighting parameter is the numerator. A small change in the numerator will cause a bigger change if the denominator is smaller. 4.9 VAV fan power (FP) Fan power in WinAM 4.3 is described in. A lower value means less power will be used for the flow rate at a thousand cubic feet per minute. In other words, the lower the value, the more energy efficient the fan will be. 67

85 Error percentage for electric Electric 6% VAV fan power 4% 2% % -4% -6% Outside air temperature F 1.3 hp/kcfm 1.2 hp/kcfm 1.1 hp/kcfm 1 hp/kcfm 0.9 hp/kcfm 0.8 hp/kcfm 0.7 hp/kcfm Figure 4-48 Electric consumption for the parameter FP, SDVAV with the economizer Figure 4-48 shows that when the fan power has been adjusted from 1.3 to 0.7, the electric consumption will be reduced. This is because the fan is more efficient, so less energy will be used to create the same amount of airflow. 68

86 Influence coefficient 2 18% 16% 14% 12% 1 8% 6% 4% 2% FP - electric IC vs. OAT W-IC 6% 6% 7% 7% 8% 8% 1 12% 12% 16% 17% 18% W/O-IC 6% 6% 7% 7% 8% 8% 1 12% 12% 16% 17% 18% Figure 4-49 Electric consumption IC value for the parameter FP Figure 4-49 shows that the fan power parameter is more sensitive in warm OATs than in cold OATs. 69

87 Error percentage for chilled water Error percentage for chilled water Chilled water 3% VAV fan power 2% 1% % -2% -3% Outside air temperature F 1.3 hp/kcfm 1.2 hp/kcfm 1.1 hp/kcfm 1 hp/kcfm 0.9 hp/kcfm 0.8 hp/kcfm 0.7 hp/kcfm Figure 4-50 Chilled water consumption for the parameter FP, SDVAV with the economizer 2% VAV fan power 1% 1% % -1% -2% Outside air temperature F 1.3 hp/kcfm 1.2 hp/kcfm 1.1 hp/kcfm 1 hp/kcfm 0.9 hp/kcfm 0.8 hp/kcfm 0.7 hp/kcfm Figure 4-51 Chilled water consumption for the parameter FP, SDVAV without the economizer 70

88 Influence coefficient Figure 4-50 and Figure 4-51 are the chilled water consumption for the system with the economizer and the system without the economizer. The pattern of the results for the chilled water consumption for both systems is similar. The smaller the number in the legend, the higher the efficiency of the fan. When the fan power increases, the chilled water consumption decreases simultaneously. This can be explained by the fact that the temperature across the fan changes when adjusting the fan power. Figure 4-52 shows that the difference exists when average monthly OAT falls below When fan power changes, the temperature across the fan will also be simultaneously changed. For the system with the economizer, free cooling will be used. 9% 8% 7% 6% 5% 4% 3% 2% 1% FP - chilled water IC vs. OAT W-IC 8% 5% 5% 5% 5% 4% 4% 4% 4% 4% 4% 4% W/O-IC 4% 4% 4% 4% 4% 4% 4% 4% 4% 4% 4% 4% Figure 4-52 Chilled water consumption IC value for the parameter FP 71

89 Error percentage for electric Hot water The fan power parameter does not impact the hot water consumption Nighttime lighting and plug load ratio (NLPL) This parameter is used for deciding the lighting and plug load ratio at night. The impact caused by this parameter is similar to the impact caused by the lighting power Electric 6% 4% 2% % % -6% Nighttime lighting and plug load ratio Outside air temperature F 26% 24% 22% 2 18% 16% 14% Figure 4-53 Electric consumption for the parameter NLPL, SDVAV with the economizer 72

90 Influence coefficient NLPL - electric IC vs. OAT 16% 16% 15% 15% 14% 14% 13% W-IC 16% 16% 16% 15% 15% 15% 15% 15% 15% 14% 14% 14% W/O-IC 16% 16% 16% 15% 15% 15% 15% 15% 15% 14% 14% 14% Figure 4-54 Electric consumption IC value for the parameter NLPL Figure 4-53 shows that the electric consumption is decreased by decreasing the NLPL power. The physical explanation is the same as that of the plug load parameter. Figure 4-54 implies that the electric influence coefficients caused by adjusting the NLPL parameter are the same for both the system with the economizer and the system without the economizer. The physical explanation of the impact to the electric consumption caused by the NLPL is the same as the physical explanation of the lighting parameter. The cooling load in the zone will be decreased when reducing the interior zone cooling load. If the temperature difference between the mixed air and the supply air is not changed, the supply airflow rate will be reduced. This means that the fan can work less. 73

91 Influence coefficient Error percentage for chilled water Chilled water Nighttime lighting and plug load ratio 1% 1% % -1% Outside air temperature F 26% 24% 22% 2 18% 16% 14% Figure 4-55 Chilled water consumption for the parameter NLPL, SDVAV with the economizer NLPL - chilled water IC vs. OAT 3% 3% 2% 2% 1% 1% W-IC 1% 1% 1% 1% 2% 2% 2% 3% 3% 2% 2% 2% W/O-IC 1% 1% 1% 1% 2% 2% 3% 3% 2% 2% 2% Figure 4-56 Chilled water consumption IC value for the parameter NLPL 74

92 Error percentage for hot water The chilled water consumption results impacted by adjusting the NLPL parameter can be observed in Figure When reduce the NLPL parameter, the chilled water consumption will reduced. From Equation 4-1, reducing the NLPL parameter the heating load in the zone will be reduced, so the cooling load required in cooling coil will be reduced. That is why when reducing the NLPL parameter, the chilled water consumption will be reduced. The chilled water consumption is more sensitive in the cold OATs for the system with the economizer, see Figure The economizer will make the system use less water in the temperature range of 30 to Hot water 15% 1 5% % % Nighttime lighting and plug load ratio Outside air temperature F 14% 16% 18% 2 22% 24% 26% Figure 4-57 Hot water consumption for the parameter NLPL, SDVAV with the economizer 75

93 Influence coefficient Figure 4-57 shows when there is an increase in the NLPL parameter, the hot water consumption will be decreased. This is because increasing the NLPL parameter will increase the interior heat gain. -5% -1-15% -2-25% -3-35% -4-45% -5 NLPL - hot water IC vs. OAT W-IC -7% -8% -12% -13% -12% -19% -18% -34% -36% -42% -43% -43% W/O-IC -7% -8% -12% -13% -12% -19% -18% -34% -36% -42% -43% -43% Figure 4-58 Hot water consumption IC value for the parameter NLPL Figure 4-58 shows that the hot water consumption influence coefficient values are impacted more by adjustment to the NLPL parameter than adjustment to the lighting parameter. This is because hot water will be consumed more in the nighttime compared with the daytime Wall R-value (Wall R) The IC values for this parameter by adjusting around the base model are all 0. 76

94 Error percentage for electric Electric Exterior wall R-value 1% Outside air temperature F 8.4 (sf*hr* )/Btu 9.6 (sf*hr* )/Btu 10.8 (sf*hr* )/Btu 12 (sf*hr* )/Btu 13.2 (sf*hr* )/Btu 14.4 (sf*hr* )/Btu 15.6 (sf*hr* )/Btu Figure 4-59 Electric consumption for the parameter wall R, SDVAV with the economizer Figure 4-59 shows that adjusting the R-value of the wall within around the baseline does not have the obvious impact on the electric consumption. 77

95 Error percentage for chilled water Chilled water Exterior wall R-value 3% 2% 2% 1% 1% % % -2% Outside air temperature F 8.4 (sf*hr* )/Btu 9.6 (sf*hr* )/Btu 10.8 (sf*hr* )/Btu 12 (sf*hr* )/Btu 13.2 (sf*hr* )/Btu 14.4 (sf*hr* )/Btu 15.6 (sf*hr* )/Btu Figure 4-60 Chilled water consumption for the parameter wall R, SDVAV with the economizer. Figure 4-60 shows that when the R-value of the wall is decreased, chilled water consumption will be increased. This is because the lower the R-value, the less thermal resistance there will be. Equation 4-6 can be used to explain this phenomenon. When reducing the R-value, the heat transfer rate will be increased. This means that the lower the R-value, the quicker the heat flow will get through the wall. The physical explanation for this parameter is similar to the physical explanation of the window s U- value. The effects caused by this wall R-value parameter to the chilled water consumption are the same for the system with the economizer and the system without the economizer. R-value is the reciprocal of U-value. 78

96 Error percentage for hot water Hot water 8% Exterior wall R-value 6% 4% 2% % % -6% Outside air temperature F 8.4 (sf*hr* )/Btu 9.6 (sf*hr* )/Btu 10.8 (sf*hr* )/Btu 12 (sf*hr* )/Btu 13.2 (sf*hr* )/Btu 14.4 (sf*hr* )/Btu 15.6 (sf*hr* )/Btu Figure 4-61 Hot water consumption for the parameter wall R, SDVAV with the economizer Figure 4-61 shows the hot water consumption results impacted by adjusting the R-value of the wall. As explained in the chilled water section for this parameter, lower R-value means higher heat transfer rate. Under this condition, more hot water is required to keep the room temperature at its setpoint. 79

97 Error percentage for electric 4.12 Window U-value (Window U) Electric 2% Exterior window U-value 1% 1% % % -2% -2% Outside air temperature F Btu/(sf *hr* ) 0.9 Btu/(sf *hr* ) Btu/(sf *hr* ) 0.75 Btu/(sf *hr* ) Btu/(sf *hr* ) 0.6 Btu/(sf *hr* ) Btu/(sf *hr* ) Figure 4-62 Electric consumption for the parameter window U, SDVAV with the economizer The effect to the system caused by the window U-value parameter is similar to the wall R-value effect, as shown in Figure But the effect caused by adjusting the U-value within is more compared with adjusting the R-value. When increasing the U-value (decreasing the R-value) of the window, the electric consumption will be increased. Equation 4-6 explains that when increasing the U-value, the heat transfer rate will be increased. This means that the OAT will have more of an effect on the zone temperature when the zone has the higher U-value window compared to when the zone has the lower U-value window. 80

98 Influence coefficient 5% 4% 4% 3% 3% 2% 2% 1% 1% Window U - electric IC vs. OAT W-IC 1% 1% 1% 1% 2% 3% 3% 4% 4% 4% W/O-IC 1% 1% 1% 1% 2% 3% 3% 4% 4% 4% Figure 4-63 Electric consumption IC value for the parameter window U Figure 4-63 implies that the window U-value parameter is more sensitive in the hot OAT, and that the effect caused by this parameter is the same for both systems. From Equation 4-6, the temperature difference between the average monthly OAT and the zone temperature in the cold OATs can be as high as 27, and as high as 18 in the hot OATs. This parameter is less sensitive to electric consumption in the cold OATs because the supply air temperature is 50. This is close to the coldest average OAT, but very far from the hottest average OAT. 81

99 Error percentage for chilled water Chilled water 6% Exterior window U-value 4% 2% % % -6% -8% Outside air temperature F Btu/(sf *hr* ) 0.9 Btu/(sf *hr* ) Btu/(sf *hr* ) 0.75 Btu/(sf *hr* ) Btu/(sf *hr* ) 0.6 Btu/(sf *hr* ) Btu/(sf *hr* ) Figure 4-64 Chilled water consumption for the parameter window U, SDVAV with the economizer Figure 4-64 shows that chilled water consumption is impacted by adjusting the U-value of the window. This can be explained as similar to the input parameter s impact on electric. 82

100 Influence coefficient Window U - chilled water IC vs. OAT 25% 2 15% 1 5% W-IC 1% 4% 5% 5% 9% 1 14% 16% 17% 18% 19% 19% W/O-IC 2% 4% 4% 7% 9% 13% 16% 17% 18% 19% 19% Figure 4-65 Chilled water consumption IC value for the parameter window U Figure 4-65 implies that chilled water consumption for the system with the economizer is more sensitive than the system without the economizer in cold OATs. 83

101 Influence coefficient Error percentage for hot water Hot water Exterior window U-value 25% 2 15% 1 5% -5% % -2-25% Outside air temperature F Btu/(sf *hr* ) 0.9 Btu/(sf *hr* ) Btu/(sf *hr* ) 0.75 Btu/(sf *hr* ) Btu/(sf *hr* ) 0.6 Btu/(sf *hr* ) Btu/(sf *hr* ) Figure 4-66 Hot water consumption for the parameter window U, SDVAV with the economizer Window - hot water IC vs. OAT W-IC 62% 6 52% 5 52% 39% 41% 8% 4% 1% -1% W/O-IC 62% 6 52% 5 52% 39% 41% 8% 4% 1% -1% Figure 4-67 Hot water consumption IC value for the parameter window U 84

102 Error percentage for electric Figure 4-66 shows the effect on hot water consumption by adjusting window U-value, is similar to the U-value effect of electric and chilled water consumption. When the window U-value is increased, the hot water consumption will be increased. The sensitivity level is the opposite compared to the electric consumption and the chilled water consumption, see Figure Roof U-value (Roof U) Electric Roof U-value Outside air temperature F Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) 0.48 Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) Figure 4-68 Electric consumption for the parameter roof U, SDVAV with the economizer The impact to the electric consumption by adjusting the U-value within is under 1%. The system with the economizer and the system without the economizer have a similar pattern of results for electric consumption at each potential roof U-value, (Figure 85

103 4-69). Figure 4-68 is the general pattern of the results by adjusting the roof U-value which is different from the pattern of results by adjusting the window-wall ratio (Figure 4-11), wall R-value (Figure 4-59) and window U-value (Figure 4-62). This difference is seen because the project has been divided into interior and exterior zones. The other parameters with U-values and R-values only connect with the exterior zone, but the roof U-value is relative to both the interior zone and the exterior zone. Equation 4-6 shows that the higher the R-value is the lower the heat transfer rates will be. A lower U-value indicates the less heat transfer rate through the components. If this occurs in hot OATs, the higher the R-value is the cooler the zone will be, and less electric energy will be consumed through the fan. In cold OATs, although the interior zone and the exterior zone can be kept at zone temperature setpoint, the interior zone needs to be cooled down. The heat transfer rate is low when the roof U-value is high. In this case, more electric energy will be consumed by the fan. 86

104 Influence coefficient 1% 1% 1% 1% -1% -1% Roof U electric IC vs. OAT W-IC -1% 1% 1% 1% 1% 1% W/O-IC -1% 1% 1% 1% 1% 1% Figure 4-69 Electric consumption IC value for the parameter roof U Figure 4-69 implies that adjusting the roof R-value does not cause a difference in electric consumption between the system with the economizer and the system without the economizer. 87

105 Error percentage for chilled water Chilled water Roof U-value 1% 1% 1% % -1% -1% Outside air temperature F Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) 0.48 Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) Figure 4-70 Chilled water consumption for the parameter roof U, SDVAV with the economizer Figure 4-70 shows the chilled water consumption results by roof U-value adjustment. The results pattern obtained by adjusting the roof U-value is different from the pattern of the results obtained by adjusting the window-wall ratio (Figure 4-13), the wall R-value (Figure 4-60) and window U-value (Figure 4-60). The reason for this difference is explained in Section Electric. Figure 4-70 and Figure 4-71 show the chilled water consumption for the system with the economizer and the system without the economizer. These two systems have different patterns of chilled water consumption in cold OATs. Figure 4-72 demonstrates this with solid data evidence. 88

106 Influence coefficient Error percentage for chilled water Roof U-value 2% 1% 1% % -1% -2% Outside air temperature F Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) 0.48 Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) Figure 4-71 Chilled water consumption for the parameter roof U, SDVAV without the economizer 4% 3% 2% 1% -1% -2% -3% -4% -5% Roof U - chilled water IC vs. OAT W-IC -2% 1% 1% 2% 2% 3% 3% 3% W/O-IC -4% -3% -1% -1% -1% 1% 2% 2% 3% 3% 3% Figure 4-72 Chilled water consumption IC value for the parameter Roof U 89

107 Influence coefficient Error percentage for hot water Hot water Roof U-value 8% 6% 4% 2% % % -6% -8% Outside air temperature F Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) 0.48 Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) Btu/(sf *hr* ) Figure 4-73 Hot water consumption for the parameter roof U, SDVAV with the economizer Roof U - hot water IC vs. OAT 1 5% -5% -1-15% -2-25% W-IC 6% 6% 5% 5% 5% 3% 3% -4% -6% -17% -19% -22% W/O-IC 6% 6% 5% 5% 5% 3% 3% -4% -6% -17% -19% -22% Figure 4-74 Hot water consumption IC value for the parameter roof U 90

108 Error percentage for electric Figure 4-73 shows the results for hot water consumption at different roof U-value adjustments. Figure 4-74 indicates that the effects of adjusting the roof U-value are the same for both the system with the economizer and the system without the economizer. The effect on hot water consumption made by adjusting the roof U-value can be explained in the opposite way compared with its effect to electric Peak occupancy (Occ) Peak occupancy parameter shows the average area per person, in this way the higher this parameter is the less the number of people will be in the zone Electric Peak occupancy 1% 1% 1% % Outside air temperature F 105 sf/person 120 sf/person 135 sf/person 150 sf/person 165 sf/person 180 sf/person 195 sf/person Figure 4-75 Electric consumption for the parameter Occ, SDVAV with the economizer 91

109 Influence coefficient Occ - electic IC vs. OAT -1% -1% -2% -2% -3% W-IC -1% -1% -1% -1% -1% -2% -2% -2% -2% -2% -2% -2% W/O-IC -1% -1% -1% -1% -1% -2% -2% -2% -2% -2% -2% -2% Figure 4-76 Electric consumption IC value for the parameter Occ Figure 4-75 implies that when the peak occupancy parameter is increased, the electric consumption will be reduced. Figure 4-76 shows that the effect to the electric influence coefficient caused by adjusting the occupancy parameter is the same in both the system with the economizer and the system without the economizer. Occupancy loads will affect the interior heat gain; the lower the peak occupancy parameter value is, the less the heat gain. From Equation 4-1 peak occupancy parameter. Meanwhile, the is decreased because of increasing the is constant, so the only way to balance this equation is to reduce the. From Equation 4-2 to Equation 4-4, decreasing the flow rate means decreasing the actual fan power, that is why the electric consumption will be decreased. 92

110 Error percentage for chilled water Chilled water 8% Peak occupancy 6% 4% 2% % -4% Outside air temperature F 105 sf/person 120 sf/person 135 sf/person 150 sf/person 165 sf/person 180 sf/person 195 sf/person Figure 4-77 Chilled water consumption for the parameter Occ, SDVAV system with the economizer A similar physical explanation to that used in electric consumption for adjusting the occupancy parameter can be applied to chilled water consumption. When increasing the peak occupancy parameter, the peak value for the people in this zone will be reduced. In this way, the heat gain in this zone will be reduced, so the chilled water consumption will be reduced, see Figure The influence coefficients at different adjustment for this parameter are shown in Figure

111 Error percentage for hot water Influence coefficient -2% -4% -6% -8% -1-12% -14% -16% -18% Occ - chilled water IC vs. OAT W-IC -13% -14% -15% -16% -15% -14% -13% -12% -12% -9% -9% -9% W/O-IC -14% -12% -14% -16% -15% -14% -14% -12% -12% -9% -9% -9% Figure 4-78 Chilled water consumption IC value for the parameter Occ Hot water 3% Peak occupancy 2% 1% % % -3% -4% -5% Outside air temperature F 195 sf/person 180 sf/person 165 sf/person 150 sf/person 135 sf/person 120 sf/person 105 sf/person Figure 4-79 Hot water consumption for the parameter Occ, SDVAV system with the economizer 94

112 Influence coefficient 1 9% 8% 7% 6% 5% 4% 3% 2% 1% Occ - hot water IC vs. OAT W-IC 4% 4% 6% 6% 6% 8% 8% 9% 9% 9% 9% 9% W/O-IC 4% 4% 6% 6% 6% 8% 8% 9% 9% 9% 9% 9% Figure 4-80 Hot water consumption IC value for the parameter Occ Figure 4-79 and Figure 4-80 show that the impact to the hot water consumption by the peak occupancy parameter is the same for the system with the economizer and the system without the economizer. The more people in the zone, the more heat gain the system will receive and the less hot water will be required. 95

113 5. CALIBRATION TASK 5.1 Austin City Hall Background (Zhou et al. 2009) For this project, two software programs were used to model the same project. One is modeled by equest 3.64 and the other is modeled by WinAM 4.3. The purpose for simulating two models is to have the equest 3.64 model as the comparison model. There are two reasons for choosing equest 3.64: 1) equest 3.64 is one of the most popular software programs for building performance simulation, and 2) Compared with WinAM 4.3, the interface of equest 3.64 is simpler. Austin City Hall is located in downtown Austin, Texas. All of the information generated from this project is mainly based on the Continuous Commissioning (CC ) report and some of the information comes from the discussion with the engineer who did the CC project for Austin City Hall. There are four floors in Austin City Hall. The total area is approximately 115,000 square feet. The envelope information for this building is in Table 5-1. Table 5-1 Envelope information of Austin City Hall Item Content U-value of the wall U-value of the roof U-value of the window

114 There are ten AHUs in this building. The hot water comes from the boiler on site. The efficiency of the boiler is approximately 8. The chilled water used in the HVAC system is purchased. The chilled water pump power and the hot water pump power are 20 horsepower separately. The extra energy consumed here is used by the exhaust fan and the lighting in the parking garage. The energy consumed by the lighting in the parking garage is approximately 63 kw from 6:00 p.m. to 6:00 a.m. The exhaust fan power usage documented in the report is 300 kw. If the reduction factor of 0.4 is applied to the fan, 120 kw should be used in WinAM 4.3. The general information for the air side system is offered in Table 5-2. Table 5-2 Air side system information of Austin City Hall Item Content Space temperature setpoint 72 Minimum primary flow 0.2 Maximum primary flow 1.1 Minimum outside airflow 2 Preheat coil setpoint When the OAT is lower than 55, the preheat coil temperature setpoint will be 69. Precool coil setpoint 55 When the OAT is higher than 70 Cooling coil reset schedule limitation should be 55. When the OAT is lower than 40 limitation should be 65. Fan power 0.85 Peak weekly lighting usage 1.4 Peak weekly plug load 0.75, the cooling coil s high, the cooling coil s low 97

115 Item Nighttime lighting and plug load ratio Weekday peak load ratio Weekend peak load ratio 0.3 Weekday operating hours for lighting and plug Table 5-2 continued 8:00 a.m. to 5:00 p.m. Content To simplify the modeling process, ten AHUs in Austin City Hall will be divided into three groups based on system type and operation schedule. AHU 1, 2, 3, 4, 5, 6, 7 and 9 are Single Duct Variable Air Volume AHUs (SDVAV). AHU 8 and 10 are Single Zone Single Duct Constant Air Volume AHUs (SZSDCAV). The schedules for AHU 1, 9 and AHU 2, 3, 4, 5, 6, 7 are different. Under these conditions, there will be three different AHU groups. AHU Group 1 includes AHU 1 and AHU 9. AHU Group 2 includes AHU 8 and AHU 10. AHU Group 3 includes AHU 2, 3, 4, 5, 6, and 7. The remaining information for each AHU group is in Table 5-3, Table 5-4, and Table 5-5. Table 5-3 AHU Group 1: AHU 1 and AHU 9 of Austin City Hall Category Item Content Building envelope information Conditioned floor area Interior zone percentage 85% Exterior wall and window area 3654 Window percentage 15% Roof area 0 16% of the total building area 98

116 Table 5-3 continued Category Item Content Schedules and loads Normal weekday schedule for AHUs 24/7 Normal weekend schedule for AHUs 24/7 Peak weekly occupancy 50 Weekday operating hours for lighting and plug 12:00 p.m. to 3:00 p.m. Table 5-4 AHU Group 2: AHU 8 and AHU 10 of Austin City Hall Category Item Content Building envelope information Schedule and load Conditioned floor area Interior zone percentage 25% Exterior wall and window area 2772 Window percentage 25% Roof area 0 Normal weekday schedule for AHUs Normal weekend schedule for AHUs Peak weekly occupancy 40 Weekday operating hours for lighting and plug 6% of total building area 12:00 a.m. to 5:30 a.m. off off Table 5-5 AHU Group 3: AHU 2, 3, 4, 5, 6, and 7 of Austin City Hall Category Item Content Building envelope information Schedule and load Conditioned floor area Interior zone percentage 75% Exterior wall and window area Window percentage 55% Roof area Normal weekday schedule for AHUs Normal weekend schedule for AHUs 78% of total building area 6:00 a.m. to 10:00 p.m. off 99

117 Table 5-5 continued Category Item Content Schedule and load Modeling process Peak weekly occupancy 130 Weekday operating hours for lighting and plug off Base models for equest 3.64 and WinAM 4.3 a. WinAM 4.3 model Create WinAM 4.3 model a.1winbasemodel based on the information mentioned previously. b. Create equest 3.64 model a.1equest3.64basemodel Some of the information cannot be applied to equest 3.64 directly, because some options in equest 3.64 are not exactly the same as in WinAM 4.3. Under this condition, multiple changes are required to model Austin City Hall in equest The following is a general summary of the differences between equest 3.64 and WinAM 4.3 on the Austin City Hall project. Zone assignment In WinAM 4.3, the zone will be assigned based on the different percentage of exterior or interior zone. Figure 5-1 denotes in equest 3.64 the exterior zone is from the exterior wall to the inner space with the user deciding the depth. 100

118 Figure 5-1 equest 3.64 zone assignment Thermal mass equest 3.64 asks for thermal mass, while WinAM 4.3 only asks for R-value. To make equest 3.64 run the material with the lowest specific heat, the library has been chosen. The roof gravel has a specific heat of 0.4 and a thickness of 0.5. Window Glass transmittance equest 3.64 requires glass visible transmittance and the shading coefficient for completing the windows inputs, while WinAM 4.3 only requires the U-value. Window area equest 3.64 asks for the window area for each wall, while WinAM 4.3 requires the window percentage for each AHU. In equest 3.64 window areas of 42.7% 101

119 for each wall will be used. The window area for each AHU group in WinAM 4.3 has been discussed in Table 5-3, Table 5-4 and Table 5-5. Plenum equest 3.64 requires the users to consider plenum. WinAM 4.3 does not have this option. Preheat equest 3.64 asks for the ΔT (the temperature difference between before and after the hot water goes through the reheat coil), while the reheat temperature for WinAM 4.3 will be as high as what is needed to meet the needs of the heating load. Austin City Hall requires preheat reset in the real project. WinAM 4.3 can achieve this requirement easily by using the preheat coil reset. The Building Creation Wizard level of equest 3.64 only supports the constant preheating option. Outside airflow rate The outside airflow rate in equest 3.64 has been set to default based on the different activity area. In WinAM 4.3 the user can decide it themselves. System assignment equest 3.64 allows the users to have at most 2 systems and 3 schedules for each system in the basic level. WinAM 4.3 has only one schedule for each certain system, but more than 2 systems can be assigned. This makes the schedule assignment for the two programs significantly different from each other. 102

120 Apply CC measures to equest 3.64 and WinAM 4.3 model The model without calibration and that has CC measures has been named a.2winbasemodel in WinAM 4.3 and a.2equest3.64basemodel in equest The CC measures that have been applied to both the WinAM 4.3 and equest 3.64 models are listed in Table 5-6. Table 5-6 CC measures for Austin City Hall Item AHU Before CC Group Measures After CC Measures Outside airflow% All 2 1 Minimum airflow rate All 0.2 CFM/ft CFM/ft 2 Economizer All None 37 to 64 Preheat All If it is in the occupied mode, the Minimum airflow rate AHU minimum should be 0.14 CFM/ft 2. If it 0.2 CFM/ft 2 Group1 is in the unoccupied mode, the minimum should be 0 CFM/ft 2. Due to the properties of WinAM 4.3, some CC measures cannot be applied to the simulated project. Demand-controlled ventilation using CO 2 sensors, static pressure reset and others are measures that cannot be applied. These measures can be the reason that the simulated data does not agree with the measured data. 103

121 Calibrate WinAM 4.3 model Measured data is used to calibrate the WinAM 4.3 model. The same calibration steps that have been used in the WinAM 4.3 model are applied to equest 3.64 models. The calibrated WinAM 4.3 model is named b.1winbasemodel. The calibrated equest 3.64 model is named b.1equest3.64basemodel. The method to generate weather data for WinAM 4.3 and equest 3.64 has been discussed in Section 3 and Appendix A. The detailed calibration steps are shown in Table 5-7. Table 5-7 Calibration for Austin City Hall Item AHU Group Before Calibration After Calibration Non-HVAC electric usage (24/7) Minimum airflow rate Cooling coil reset All 120 kw 140 kw AHU Group CFM/ft CFM/ft 2 AHU Group CFM/ft CFM/ft 2 AHU Group 1 AHU Group 3 Temperature setpoint for the lower OAT is 65. Temperature setpoint for the lower OAT is 65. Peak plug load All 1 W/ft W/ft 2 Outside airflow percentage Peak Occupancy Humidity upper limitation All 2 25% Temperature setpoint for the lower OAT is 60 Temperature setpoint for the lower OAT is 60 AHU Group 1 50 ft 2 /person 100 ft 2 /person AHU Group 2 30 ft 2 /person 60 ft 2 /person AHU Group ft 2 /person 100 ft 2 /person AHU Group 2 65% 6 104

122 Apply CC measures to the calibrated model The same CC measures documented in Section are applied to the WinAM 4.3 model and equest 3.64 model b.1winbasemodel and b.1equest 3.64basemodel. The new model WinAM 4.3 model is named b.2winbasemodel. The new equest 3.64 model is named b.2equest3.64basemodel. 5.2 Dallas/Fort Worth (DFW) International Airport Terminal D Background (ESL 2010a) Dallas/Fort Worth (DFW) International Airport Terminal D is a 160,000 square foot building. It has a special structure inside such that there is no complete floor between the second floor and the third floor. Figure 5-2 shows the inside of the building structure from the north. Figure 5-3 is the west facing chart. The challenge for modeling this project in WinAM 4.3 is that the area served by each AHU is not documented in the CC report. A solution that allows for analysis is to sum the maximum airflow rate from each terminal box for the separated AHU. Then the percentage of each AHU group s total maximum airflow rate is calculated for comparison with the total airflow rate supplied to this building. 105

123 Figure 5-2 Internal structure of the north face of Terminal D Figure 5-3 Internal structure of the west face of Terminal D 106

124 DFW is served by VAV AHUs, SDCAV AHUs and Outside Air AHUs. The outside air that supplies the VAV AHUs and CAV AHUs comes from the Outside Air AHUs. Based on the Continuous Commissioning of Terminal D DFW International Airport Final Report for September 2010, there are two AHU groups for the WinAM 4.3 model. The cooling and heating resource come from the plant on site. The envelope information is in Table 5-8. Table 5-8 Envelope information of DFW Terminal D Item Content U-value for wall U-value for roof U-value for window 0.75 The general information for the air side system is in Table 5-9: Table 5-9 Air side system data of DFW Terminal D Item Content Space setpoint 72 Minimum primary airflow rate 0 Maximum primary airflow rate 2.15 Preheat coil setpoint 40 Precool coil setpoint 55 Fan power 0.8 AHU schedule Peak weekly lighting usage Occupied: 5:00 a.m. to 10:00 p.m. Unoccupied: 10:00 p.m. to 5:00 a.m.

125 Table 5-9 continued Item Content Peak weekly plug load 0.7 Peak weekly occupancy 333 Nighttime lighting and plug load ratio 0.2 Weekday peak load ratio 1 Weekend peak load ratio 1 Weekday operating hours for lighting and plug 7:00 a.m. to 8:00 p.m. Weekend operating hours for lighting and plug 7:00 a.m. to 8:00 p.m. The AHUs in this project have been divided into two groups. The information for AHU Group 1 is in Table 5-10; the information for AHU Group 2 is in Table Table 5-10 AHU Group 1: VAV AHUs of DFW Terminal D Category Item Content Building envelope information Schedules and loads Conditioned floor area Interior zone percentage 75% Exterior wall and window area Window percentage 7 Roof area Normal weekday schedule for AHUs Normal weekend schedule for AHUs 7 of total building area 24/7 24/7 108

126 Table 5-10 continued Category Item Content Secondary system Cooling coil setpoint Minimum OA flow When the OAT is higher than 65, the cooling coil high temperature limitation should be 55. When the OAT is lower than 45, the cooling coil temperature low limitation should be 60. Unoccupied: Occupied: 26% Table 5-11 AHU Group 2: SDCAV AHUs of DFW Terminal D Category Item Content Building envelope information Schedules and loads Secondary system Conditioned floor area Interior zone percentage 75% Exterior wall and window area Window percentage 7 Roof area Normal weekday schedule for AHUs Normal weekend schedule for AHUs 3 of total building area 24/7 24/7 Cooling coil setpoint 55 Minimum OA flow Unoccupied: Occupied: 15% 109

127 5.2.2 Modeling process Base model for WinAM 4.3 The base model is simulated as WinAM 4.3 model a.1basemodel according to the information collected above Apply CC measures to WinAM 4.3 model CC measures are applied to a.1basemodel and the new model is named a.2basemodel. The CC measures for this project are listed in Table Item Table 5-12 CC measures of DFW Terminal D AHU Before CC After CC Measures Group Measures Minimum airflow rate All 0.14 Add the occupied /unoccupied mode, so the minimum airflow rate can be 0. When the OAT is lower than 45, the cooling Cooling coil reset All 55 coil temperature setpoint is 60. When the OAT is higher than 65, the cooling coil temperature setpoint is 55. Although in Table 5-12 there are only two CC measures applied to the WinAM 4.3 simulated model, more CC measures have been applied to this project. WinAM 4.3 cannot model these additional measures. The reason is that the improvement measures 110

128 are different for each single AHU, and in this project there are 15 different improvements that have been applied to more than 50 AHUs Calibrate WinAM 4.3 model Model b.1 basemodel is calibrated based on the measured data. The calibration steps are listed in Table Table 5-13 Calibration steps for DFW Terminal D Item AHU Group Before Calibration After Calibration Minimum airflow rate AHUVAV Outside airflow % AHUVAV 26% 2 Peak plug load W/ft 2 All 0.75 W/ft 2 1 W/ft 2 Night plug load ratio All After the calibration, the new calibrated base model is renamed as b.1basemodel Apply CC measures to the calibrated model Apply the same CC measures to the calibrated base model b.1basemodel, and name this new model b.2basemodel. 111

129 5.3 DFW International Airport Rent-A-Car Center Background (Zeig et al. 2004) The DFW International Airport Rent-A-Car Center has an area of 130,000 square feet with two stories. The building attached to it is the two-story parking garage. Both of the buildings run 24/7. Although the parking garage building is not air-conditioned, it is one of the top 3 power consuming facilities in the DFW International Airport because the lighting in the garage is always on. There are six SDVAV AHUs named from AHU1 to AHU6, and more than 133 terminal boxes serve this building. In the WinAM 4.3 model, these six AHUs will be combined into one AHU group, because they have the same system and similar performance. Compared with the other AHUs, AHU 4 and AHU 5 have different static pressure. Since WinAM 4.3 cannot model statistic pressure, they are still grouped with the other AHUs. The general information for this project is in Table 5-14: Table 5-14 General information for DFW Rent-A-Car Center Category Item Content U-value for wall Envelope U-value for roof U-value for window 0.75 Conditioned floor area Interior zone percentage 8 112

130 Table 5-14 continued Category Item Content Envelope Roof Area Exterior wall and window area Window percentage 3 First system Chiller efficiency Chilled water pumping power 74 Minimum airflow 0.36 Maximum airflow 1.2 Minimum outside airflow percentage 3 Second system Preheat coil setpoint 45 Cooling coil setpoint 55 Space setpoint 73 Supply fan power 0.8 Return fan power 0.4 Peak weekly occupancy 83 Peak weekly lighting usage 1 Internal loads Peak weekly plug load 1.2 Nighttime lighting and plug load ratio 0.8 Weekday peak load ratio 1 Lighting and plug weekday operating hours 24/7 Schedule Lighting and plug weekend operating hours 24/7 AHU weekday schedule 24/7 AHU weekend schedule 24/7 113

131 Suppose this project operates 12 hours in the daytime. The total load of the second level in the south garage has been reduced to approximately 46.3 kw/day when the light is turned off during the day. That denotes that the full lighting load for the south garage is approximately twice the 46.3 kw/day. For simplification, 100 kw/day will be used instead of 92.6 kw/day. It is the same with the other garages. We assume the lighting load for each garage is 100 kw/day during the nighttime. The remaining energy cost is caused by plug loads which run 24/7. We know the lighting for the south garage is always on before applying CC measures. According to the assumptions made earlier, the garage lighting electric consumption is 100 kw/day, so three garages will operate 300 kw over the entire day. By subtracting the lighting consumption power from the total electric consumption of kw/day, the plug load consumption of kw/day is estimated. We assume the lighting affect for the total energy is =23 kw/day So the total non-hvac electric usage is ( ) kw/day= kw/day Modeling process Base model for WinAM 4.3 The basic WinAM 4.3 model named a.1basemodel is simulated according to the information offered above. 114

132 Apply CC measures to WinAM 4.3 model CC measures are applied to a.1basemodel and the new model is named a.2basemodel. Many CC measures have been applied to this project. Many of them cannot be modeled by WinAM 4.3. Table 5-15 contains the CC measures that have been applied to the WinAM 4.3 model, and Table 5-16 are those measures that cannot be modeled by WinAM 4.3. Table 5-15 CC measures for DFW Rent-A-Car Center Component AHU Group Before CC Measures After CC Measures Outside airflow percentage All 4 15% Cooling coil reset All 55 Reschedule non- HVAC electric usage When the OAT is lower than 40, the cooling coil temperature setpoint is 62 than 75 None Minimum airflow All When the OAT is higher, the cooling coil temperature setpoint is

133 Table 5-16 CC measures that cannot be applied to DFW Rent-A-Car Center AHU Before CC Component After CC Measures Group Measures When the OAT is lower than 50, the static AHU 1,2,3,6 Static Pressure Reset AHU 4, 5 Chiller Didn t mention in operation the report reset Chiller Didn t mention in temperature the report reset Condenser water 85 temperature reset Secondary pump control 12 reset pressure is 0.5 inch of water. When the OAT is higher than 80, the static pressure is 0.8 inch of water. When the OAT is lower than 50, the static pressure is 0.5 inch of water. When the OAT is higher than 80, the static pressure is 1 inch of water. When the OAT is higher than 57, the new control of sequence was added to the chiller to enable the chiller even when the vent cycle is off. When the OAT is lower than 50, the chilled water temperature is 48. When the OAT is higher than 70, the chilled water temperature is 42. When the outside wet bulb temperature is lower than 62, the condenser water temperature is 70. When the OAT is higher than 77, the condenser water temperature is 85. The maximum differential pressure setpoint is 4 when the OAT is lower than 55 and 8 when the OAT is higher than

134 Calibrate WinAM 4.3 model The model a.1basemodel is calibrated based on the measured data and is named b.1basemodel. The calibration steps are listed in Table Table 5-17 Calibration for DFW Rent-A-Car Center Component AHU Group Before Calibration After Calibration Outside airflow percentage All 4 25% Peak plug load All Minimum airflow All Zone temperature All Apply CC measures to the calibrated model The CC measures that have been applied in are applied to the calibrated WinAM 4.3 model b.1basemodel. The new model is named b.2basemodel. 5.4 DFW Terminal E Background (ESL 2010c) This project was performed before December Sky link is the main construction in Terminal E of the Dallas/Ft. Worth airport. The total area of Terminal E is approximately 718,

135 Terminal E is served by 23 SDVAV VAHUs and 4 SDCAV AHUs. Sky link in Terminal E is served by 8 SDVAV AHUs, 4 SDCAV AHUs and approximately 120 terminal boxes. The assumption has been made that all of the SDVAV AHUs have approximately similar behavior because there is no more information about the remaining SDVAV AHUs. After discussion with the engineer who has worked for this project, the following important information was obtained: SDCAV AHUs were not involved in this CC project and the communication room was not used when they applied CC measures to this project. In this case, the AHUs in this project are SDVAV AHUs. The chilled water and hot water come from the energy plaza on site. There are 4 pumps; each pump is 20 horsepower. Two are for the chilled water and the remaining are for hot water. General information of this project is in Table 5-18: Table 5-18 General information of DFW Terminal E Category Item Content U-value for wall U-value for roof Envelope U-value for window 0.75 Conditioned floor area 781,000 Interior zone percentage 4 118

136 Table 5-18 continued Category Item Content Roof area Envelope Exterior wall and window area 1,447,401 Window percentage 5 First system Second system Internal loads Chilled water pumping power 24 Hot water pumping power 24 Supply fan power 0.8 Return fan power (SDVAV) 0.1 Nighttime lighting and plug load ratio 0.5 Weekday peak load ratio 1 Schedule Lighting and plug weekday operating hours Lighting and plug weekend operating hours 5:00 a.m. to 11:00 p.m. 5:00 a.m. to 11:00 p.m. AHU weekday schedule 24/7 AHU weekend schedule 24/7 Minimum airflow 0.36 Maximum airflow 1.2 Minimum outside airflow percentage 2 Second System Preheat coil setpoint 52 Cooling coil setpoint 56 Space setpoint 62 Supply fan power 0.8 Return fan power

137 Table 5-18 continued Category Item Content Peak weekly occupancy 333 Internal Loads Peak weekly lighting usage 1.4 Peak weekly plug load 0.75 Nighttime lighting and plug load ratio 0.5 Weekday peak load ratio Modeling process Base model for WinAM 4.3 The basic WinAM 4.3 model a.1basemodel is simulated according to the information offered above Apply CC measures to WinAM 4.3 model The CC measures are applied to a.1basemodel model. The new model is named a.2basemodel There are several CC measures applied to DFW Terminal E. Not all of them may be applied to the WinAM 4.3 simulated model. Table 5-19 shows the CC measures that can be applied to the simulated model. Table 5-20 lists the CC measures that cannot be applied to the simulated model. 120

138 Table 5-19 CC measures for DFW Terminal E Component Before CC Measures After CC Measures AHU operation schedule 24/7 3:30 a.m. to 11:00 p.m. Occupied/Unoccupied When it is unoccupied, change the minimum Does not have this mode airflow from 0.36 to 0 When the OAT is lower than 40, the cooling coil temperature is 70. When the OAT is Cooling coil 56 higher than 55, the cooling coil temperature temperature reset is 61. When the OAT is higher than 80, the cooling coil temperature is 52. Zone temperature 62 Use 71.5 Table 5-20 CC measures that cannot be applied to DFW Terminal E Component Before CC Measures After CC Measures AHU operation strategy Zone temperature strategy Airflow Strategy 24/7 Occupied heating: 68 Unoccupied heating: 60 Occupied cooling: 71 Unoccupied cooling: 85 Minimum airflow for cooling: 3 of design airflow Minimum airflow for heating: 4 of design airflow 1. For the unoccupied space, only run AHUs when the space is out of temperature range. 2. Reduce AHU static pressure setpoint when it is unoccupied or light occupied. Occupied heating: 68 Unoccupied heating: 65 Occupied cooling: 73 Unoccupied cooling: 78 Minimum airflow for cooling: of design airflow Minimum airflow for heating: 3 of design airflow 121

139 Calibrate WinAM 4.3 model The model a.1basemodel is calibrated based on the measured data. The calibration process is in Table Table 5-21 Calibration strategies for DFW Terminal E Component Before Calibration After Calibration Zone temperature Maximum airflow Peak plug load Peak lighting load U-value of the window Minimum outside air 2 15% Apply CC measures to the calibrated model The CC measures used in Section are applied to the calibrated WinAM 4.3 model. The calibrated model with CC measures is named b.2basemodel. 122

140 5.5 Sunset Valley Elementary School Background (Yagua et al. 2009) This project only has one floor and is designed on the Pod Principle, as seen in Figure 5-4. The first 2 Pods, POD-1 and POD-2, plus the main POD were built in POD- 3 was added in 1984 and POD-4 was added in Figure 5-4 Floor plan of Sunset Valley Elementary School The total conditioned area is approximately 58,063. Since it is an elementary school, it has classrooms, administrative offices, a gymnasium, a cafeteria, a kitchen, a library, storage area and restrooms. CC measures have been implemented in this project since June The envelope information of Sunset Valley Elementary School is in Table 5-22: 123

141 Table 5-22 Envelope information of Sunset Valley Elementary School Component Content U-value for wall U-value for roof U-value for window 0.75 There are four Dual Duct Constant Volume Multi-zone (DDCVM) AHUs and four Single Duct Constant Volume (SDCV) AHUs. All SDCV AHUs are located in the main POD. All of them have an economizer function. While after CC measures the economizer function will be turned off for one DDCVM AHU. According to the different AHU type and control strategy applied to each AHU, they have been divided into three AHU groups in the simulated WinAM 4.3 model. AHU Group 1 contains DDCVM AHUs except the one without the economizer after CC measures. AHU Group 2 contains this DDCVM AHU. AHU Group 3 contains all SDCV AHUs. Conditioned areas in WinAM 4.3 for the different AHU groups are assigned by which POD they serve. The basic information for the first system is in Table 5-23: Table 5-23 First system information of Sunset Valley Elementary School Item Name Content Reason Electric cooling system efficiency (kw/ton) 0.88 Assumed Gas heating system efficiency (%) 8 Assumed 124

142 Chilled water pumping Hot water pumping Table 5-23 continued Item Name Content Reason 42 HP 6.9 HP The report documents that the pumping system is 70 HP. The pumping system cannot work at 10 efficiency so multiply 0.6 with 70 HP to get the reasonable input for WinAM. The report documents that the pumping system is 70 HP. The pumping system cannot work at 10 efficiency so multiply 0.6 with 11.5 HP to get the reasonable input for WinAM. General information about Sunset Valley Elementary School is in Table 5-24: Table 5-24 General information of Sunset Valley Elementary School Category Item Content Loads and schedule Nighttime lighting and plug load ratio 0 Weekday peak load ratio 1 Weekend peak load ratio 0 Weekday operating hours 7:00 a.m. to 4:00 p.m. Weekend operating hours 0 Peak weekly lighting usage 1 Peak weekly plug load 1.2 Peak weekly occupancy

143 Table 5-24 continued Category Item Content Secondary system and schedule Normal weekday schedule 5:45 a.m. to 6:25 p.m. Normal weekend schedule Off Special weekday schedule Off Special weekend schedule Off Airflow 1 Economizer (temperature) 45 to Economizer maximum OA flow 10 Hot deck coil setpoint/reset (only for DD) 95 In this project, AHUs have been divided into three groups. The information for these groups is given in Table 5-25, Table 5-26 and Table Table 5-25 Information for AHU Group 1: DDCVM AHUs of Sunset Valley Elementary School Category Item Content Envelope Secondary system Conditioned area Interior zone percentage 7 Exterior wall and window area Window percentage 3 Roof area Space setpoint 70 Minimum OA flow 2 Precool coil setpoint/ reset

144 Table 5-26 Information for AHU Group 2: DDCVM AHU (AHU-2) of Sunset Valley Elementary School Category Item Content Envelope Conditioned area 5806 Interior zone percentage 6 Exterior wall and window area 2206 Window percentage 3 Roof area 5806 Table 5-27 Information for AHU Group 3: SDCV of Sunset Valley Elementary School Category Item Content Envelope Secondary system Conditioned area Interior zone percentage 8 Exterior wall and window area 5012 Window percentage 3 Roof area Space setpoint 72 Minimum OA flow 10 Precool coil setpoint/reset 55 The way to distinguish different areas for different AHUs is based on the information offered by the CC report. All of the DDCVM AHUs provide the conditioned air for POD 1, 2 and 3. And the four SDCV AHUs serve the library, the administrative area, the gymnasium and the cafeteria which are located in the main POD and POD

145 5.5.2 Modeling process Base model for WinAM 4.3 The Sunset Valley Elementary School base model is simulated according to the information offered above. The name of the model is a.1basemodel Apply CC measures to WinAM 4.3 model The CC measures are applied to model a.1basemodel. The new model is named a.2basemodel. The CC measures in Table 5-28 are the measures which can be applied to WinAM 4.3. Table 5-28 CC measures for Sunset Valley Elementary School Item Before CC Measures After CC Measures Hot deck (DDCAV) 95 Cold deck (DDCAV) 52.5 All of the AHUs have Economizer economizers, and the temperature range for the SDCAV is 45 to 59 Chiller Operate 24/7 When the OAT is lower than 30, the hot water coil temperature is 100. When the OAT is higher than 70, the hot water coil temperature is 70. When the OAT is lower than 40, the cold water coil temperature is 65. When the OAT is higher than 60, the cold water coil temperature is 55. Eliminate the economizer model for the DDCAVM AHU-2. Operate the economizer for the SDCAV AHU in the temperature range of 40 to 60 Improve the work schedule of the chiller from 24/7 to automatic control. 128

146 Table 5-29 contains the CC measures that cannot be applied to WinAM 4.3 Table 5-29 CC measures that cannot be applied to Sunset Valley Elementary School WinAM 4.3 model Item Before CC Measures After CC Measures Boiler Boiler will be enabled when the OAT is lower than 73. Boiler will be enabled when the OAT is lower than Calibrate WinAM 4.3 model The measured data is used to calibrate the WinAM 4.3 model a.1basemodel. The calibrated model is named b.1basemodel. The calibration steps are in Table Table 5-30 Calibration for Sunset Valley Elementary School Component AHU Group Before Calibration After Calibration Window percentage AHU Group Constant primary flow AHU Group AHU Group Hot deck temperature AHU Group During the calibration process, an error of the measured data was found, one month of gas consumption was lost. The gas consumption data is in Table 5-31: 129

147 Table 5-31 Gas consumption data for Sunset Valley Elementary School Start Date End Date Measured (CCF) 5/30/2007 6/29/ /30/2007 7/28/ /29/2007 8/30/ /31/2007 9/27/ /28/ /30/ /31/ /29/ /30/ /30/ /31/2007 1/29/ /30/2008 2/27/ /28/2008 3/28/ /29/2008 4/28/ /29/2008 5/29/ The way to solve it is to use the data from a similar time period which comes from the baseline model created for this report Apply CC measures to the calibrated model The CC measures are applied to model b.1basemodel. That new model is named as b.2basemodel 130

148 5.6 DFW International Airport North Business Tower Background (ESL 2010b) This project is a four-story and 52,000 office building. It was built in This building has a rectangular floor plan which is covered with glass. The area of the second, third and fourth floors of this building is around 15,000. Three Single Duct Variable Air Volume AHUs (SDVAV AHUs) serve this building. The smallest SDVAV AHU only serves 1,800 2 of the first floor. The remaining two SDVAV AHUs serve the remaining area equally. The hot water and chilled water both come from the energy plaza on site. From the description in the report, all AHUs in this building are SDVAV, and there is no large difference between each AHU. But based on CC measures applied to this project, three AHU groups need to be created in WinAM 4.3. The only difference between these three models is that the conditioned area for each group is different. Table 5-32 General information of DFW International Airport North Business Tower Category Item Content Envelope U-value for wall U-value for roof U-value for window 0.75 Interior zone percentage 7 Exterior wall and window area Window percentage 6 131

149 Table 5-32 continued Category Item Content Envelope Conditioned floor area Loads and schedule Secondary system and schedule Nighttime lighting and plug load ratio 0.2 Weekday peak load ratio 1 Weekend peak load ratio 0.3 Weekday operating hours Weekend operating hours 7:00 a.m. to 6:00 p.m. Peak weekly lighting usage 1 Peak weekly plug load 1 Peak weekly occupancy 100 Normal weekday schedule Normal weekend schedule Special weekday schedule Special weekend schedule off 6:00 a.m. to 8:00 p.m. Maximum primary flow 1.1 Minimum primary flow ( occupied) 0.4 Minimum primary flow ( unoccupied) 0 Off Off Off Minimum outside airflow ( occupied) 2 Minimum outside airflow (unoccupied) Preheat coil setpoint 60 Cooling coil setpoint 53 Space setpoint 70 Supply fan power 0.85 Return fan power

150 5.6.2 Modeling process Base model for WinAM 4.3 The DFW International Airport North Business Tower WinAM 4.3 base model a.1basemodel is simulated based on the information offered in Section Background Apply CC measures to WinAM 4.3 model The CC measures are applied to the model a.1basemodel. The new model is named a.2basemodel. Table 5-33 shows the detailed CC measures which can be applied to WinAM 4.3. Table 5-33 CC measures that apply to the information of DFW International Airport North Business Tower Item AHU Group Before CC Measures After CC Measures When the OAT is lower than 40, the Cooling coil cooling coil temperature is 65. When AHU Group 1, 2 53 reset the OAT is higher than 75, the hot water coil temperature is 53. Return fan AHU Group 1 on off Decrease the preheat coil temperature from 60 to 40 for AHU Group 1 and 2. Preheat All 60 Decrease the preheat coil temperature from 60 to 50 for AHU Group 3. Zone temperature All

151 Table 5-34 lists the CC measures which cannot be applied to WinAM 4.3. Table 5-34 CC measures that cannot be applied to the simulated WinAM 4.3 model of DFW International Airport North Business Tower Item AHU Group Before CC Measures After CC Measures When the OAT is lower than 100, the Static AHU Group 1, static pressure is 1.5 inches of water. pressure 2 inches of water 2 When the OAT is higher than 50, the reset static pressure is 0.7 inches of water. Based on the schedule, when it is Occupied from unoccupied, the static pressure will be Occupied All 6:00 a.m. to 8:00 reduced to minimum and the supply air schedule p.m. temperature minimum side will be reduced to 55 Zone temperature All 73 reset When it is unoccupied, the zone temperature will be reset to 78 When the outside air temperature is lower Hot water than 30, the hot water temperature will supply be 160 When the OAT is higher than temperature 60, the supply hot water temperature will be 100 Hot water pump #1 will be enabled when Enable when the Hot water the OAT is lower than 63 Both of OAT is lower pump the hot water pumps will run when the than 70. OAT is lower than

152 Calibrate WinAM 4.3 model The baseline model a.1basemodel is calibrated based on the measured data. The new model is named b.1basemodel. The calibration process is listed in Table Table 5-35 Calibrations applied to the DFW International Airport North Business Tower Item AHU Group Before Calibration After Calibration Maximum primary flow AHU Group Zone temperature AHU Group Cooling coil All Peak plug AHU Group 1, Unoccupied airflow AHU Group 1, Maximum primary airflow Minimum occupied primary flow AHU Group 1, All Non-HVAC electric All 0 kw 330 kw In Table 5-35, there is no non-hvac electric cost before calibration but 330 kw after calibration. The purpose for non-hvac electric adjustment is for calibration. After calculating the savings after applying CC measures to the model, the non-hvac electric consumption will be reduced to 0 kw again Apply CC measures to the calibrated model Apply CC measures to b.1basemodel and name the new model b.2basemodel. 135

153 5.7 Blanchfield Army Community Hospital, Fort Campbell, Kentucky Figure 5-5 Overhead view of Blanchfield Army Community Hospital (Google Map 2013) 136

154 Figure 5-6 Dimensions of each building in Blanchfield Army Community Hospital (Google Map 2013) Background (Bes-Tech Inc. and ESL 2009b) The Blanchfield Army Community Hospital (BACH) is located in Kentucky, Climate Zone 4. Figure 5-5 and Figure 5-6 come from the Google map of that location. This project has 4 buildings in total. The gross area is approximately 440,000 square feet. The dimension of the project has been marked in Figure 5-6. CC measures have been applied to this hospital beginning in April 2006 and finished in December From 137

155 February, 2009 to July, 2009 the enhanced CC measures have been applied to this project. There are 18 AHUs in this project. They include 5 Single Duct Variable Air Volume AHUs, 2 Single Duct Constant Air Volume AHUs, 4 Dual Duct Constant Air Volume AHUs, 4 single zone unit AHUs, 2 multi-zone constant air volume AHUs and one roof top AHU. All of the AHUs run 24/7 except the AHU that serves the kitchen. To simplify the modeling process, this unit will be treated as if it runs 24/7 as well. This project has the plant on site. There are three chillers in total; two are 630 ton and the remaining one is 800 ton. Each chiller has one 75 horsepower chilled water pump. The cooling tower has six cells; each cell has a 25 horsepower fan. Two 60 horsepower and one 50 horsepower pumps have been applied to serve this cooling tower. There are three steam boilers on site to assist the hot water supply. The envelope information for BACH is in Table Table 5-36 Envelope information of BACH Item U-value for wall (mass) U-value for roof U-value for window 0.55 Interior zone percentage 8 Exterior wall and window area Window percentage 25% Conditioned floor area Content 138

156 The information on U-values is based on the WinAM 4.3 help manual: how to use WinAM to calculate savings from energy conservation measures (ESL 2013a). Since the information offered by the CC report is not very suitable for WinAM 4.3, almost 4 of the inputs are based on assumption. 18 AHUs have been divided into 11 AHU groups. They are separated based on the AHU system, the supply air temperature, whether they use an economizer and the cooling coil temperature setpoint. Table 5-37 is the information for AHU Group 1. Table 5-37 AHU Group 1: SDVAV with the economizer of BACH Category Item Data Loads and schedule Secondary system and schedule Nighttime lighting and plug load ratio 0.3 Weekday peak load ratio 1 Weekend peak load ratio 0.3 Lighting and plug load weekday operating hours Lighting and plug load weekend operating hours Peak weekly lighting usage 1.2 Peak weekly plug load 2.2 Peak weekly occupancy 100 AHU normal weekday schedule 24/7 AHU normal weekend schedule 24/7 Space temperature setpoint 139 4:00 a.m. to 7:00 p.m. 4:00 a.m. to 7:00 p.m. 72 F Minimum primary flow 0.3 Maximum primary flow 1 Minimum outside airflow 3

157 Table 5-37 continued Category Item Data Secondary system and schedule Economizer: temp Cooling coil setpoint Supply fan power F to 58 F When the OAT is 55 F to 40 F, the cooling coil temperature is 55 F to 57 F. Table 5-38 provides the information for AHU Group 2. The AHUs in this group are similar to the AHUs in Group 1, except for the cooling coil temperature setpoint. Table 5-38 AHU Group 2: SDVAV with the economizer of BACH Item Data Cooling coil setpoint When the OAT is 55 F to 40 F, the cooling coil temperature is 61 F to 65 F. Table 5-39 shows the information of AHU Group 3. AHU Group 3 is similar to AHU Group 1, except for the cooling coil setpoint temperature and the fact that this group does not have the economizer. Table 5-39 AHU Group 3: SDVAV without the economizer of BACH Item Data Cooling coil temperature setpoint 56 F 140

158 Table 5-40 is the information about AHU Group 4. AHU Group 4 is similar to AHU Group 1, except for the cooling coil temperature setpoint. Table 5-40 AHU Group 4: SCVAV with the economizer of BACH Item Data Cooling coil temperature setpoint When the OAT is 55 F to 40 F, the cooling coil temperature is 61 F to 65 F. Table 5-41 is the information about AHU Group 5. AHU Group 5 is similar to AHU Group 1, except for the cooling coil temperature setpoint. Table 5-41 AHU Group 5: SDCAV without the economizer of BACH Item Data Cooling coil setpoint When the OAT is 50 F to 60 F, the cooling coil temperature is 56 F to 50 F Table 5-42 gives the information about AHU Group 6. AHU Group 6 is similar to AHU Group 1, except for the temperature setpoint for the hot deck, the temperature setpoint for the cold deck, and the economizer range. Table 5-42 AHU Group 6: DDCAV with the economizer of BACH Item Data Cold deck F

159 Hot deck Economizer Item Table 5-42 continued Data 87 F 37 F to 55 F Table 5-43 gives the information about AHU Group 7. AHU Group 7 is similar to AHU Group 1, except for the temperature setpoints for the hot deck and the cold deck. Table 5-43 AHU Group 7: DDCAV without the economizer of BACH Item Data Cold deck Hot deck When the OAT is 50 F to 40 F, the cold deck temperature is 49 F to 53 F. When the OAT is 50 F to 40 F, the cold deck temperature is 75 F to 85 F. AHU Group 8: Single zone system without the economizer From the CC report, the cooling coil temperatures are different from each other for the 3 identical single zone units. To simplify the modeling process, we use one temperature for three cooling coils in WinAM 4.3. AHU Group 9: Single zone system without the economizer The only difference between AHU Group 9 and AHU Group 8 is that AHU Group 9 uses 10 outside air. 142

160 AHU Group 10: Multi-zone constant air volume system with the economizer. There is no multi-zone system in WinAM 4.3. This is because this system is no longer popular in commercial buildings, and WinAM 4.3 focuses on commercial buildings. The DDCAV system with the economizer will be used to replace the multi-zone constant air volume system in analysis performed using WinAM 4.3. Table 5-44 offers the information about AHU Group 10. The differences between AHU Group 10 and AHU Group 1 are: HVAC system type, the economizer temperature range, and the temperature setpoints for the hot deck and the cold deck. Table 5-45 offers the information of AHU Group 11. Table 5-44 AHU Group 10: Multi-zone constant air volume system with the economizer of BACH Item Data Cold deck 57 F Hot deck When the OAT is 45 F to 65 F, the cold deck temperature is 51 F to 65 F. Economizer 38 F to 58 F Table 5-45 AHU Group 11: Multi-zone constant air volume system without the economizer of BACH Item Data Cold deck 57 F When the OAT is 50 F to 40 F, the Hot deck cold deck temperature is 83 F to 85 F. 143

161 In the CC report for this project, there is no clear information about the area assigned to the different AHUs, so the assumption of assigning each AHU an equal area has been made. We make the same assumption about the assignment of the areas of the roof, the windows and the walls. Figure 5-7 SDVAV system for real CC project (Bes-Tech Inc., and ESL. 2009a) Figure 5-8 SDVAV system in WinAM

162 Figure 5-7 is the SDVAV system structure from the CC report for this project, while Figure 5-8 is the SDVAV system structure from WinAM 4.3. Comparing these two figures, the hot water coil in the real CC project is before the cooling coil; while in WinAM 4.3 only the reheat is used. The position of the fan in the real CC project is after the cooling coil, while in WinAM 4.3 the fan is before the cooling coil. Figure 5-9 SDVAV system in WinAM 4.3 with preheat Although WinAM 4.3 has the preheat option, see Figure 5-9, it is preheating for the OAT not for heating the mixed air. In the real project, the preheat coil is for heating the mixed air, as shown in Figure Modeling process Base model for WinAM 4.3 The Blanchfield Army Community Hospital base model is simulated based on the data documented earlier. That model is named a.1basemodel. There is no suitable weather 145

163 station that can be found in Kentucky, consequently a weather station in Tennessee that is within 100 miles of the Blanchfield Army Community Hospital has been used Apply CC measures to WinAM 4.3 model The CC measures are applied to the model a.1basemodel without calibrating it. This model is named a.2basemodel. Table 5-46 lists the CC measures which can be applied to the WinAM 4.3 model. AHU Group Group 1 SDVAV Group 2 SDVAV Group 3 SDVAV Table 5-46 CC measures that apply to BACH Item Before CC Measures After CC Measures Cold deck When the OAT is 55 to When the OAT is 40 to 55, the cold deck 40, the temperature temperature is 61.5 to setpoint is 55 to 57. Economizer 38 to 58 Enable the economizer when the OAT is under the range from 38 to 65. When the OAT is from 55 Cold deck to 40, the temperature When the OAT is from 40 to 55, the cold setpoint is from 61 to deck temperature is from 61.5 to Economizer From 38 to 58 Enable the economizer when the OAT is under the range from 38 to 65. Cold deck 56 When the OAT is from 40 to 55, the cold deck temperature is from 61.5 to Economizer Without the economizer Enable the economizer when the OAT is under the range from 38 to

164 AHU Group Group 4 SDCAV Group 5 SDCAV Group 6 DDCAV Group 7 DDCAV Table 5-46 continued Item Before CC Measures After CC Measures Cold deck 59 When the OAT is from 40 to 55, the cold deck temperature is from 61.5 to Economizer From 38 to 58 Enable the economizer when the OAT is under the range from 38 to 65. When the OAT is from 50 Cold deck to 60, the temperature When the OAT is 40 to 55, the cold deck setpoint is from 56 to temperature is 61.5 to Economizer None Hot deck 87 Cold deck 47 Economizer From 37 to 55 When the OAT is from 50 Hot deck Cold deck to 40, the hot deck temperature setpoint is from 75 to 85. When the OAT is from 50 to 40, the temperature Enable the economizer when the OAT is in the temperature range from 38 to 65. When the OAT is from 40 to 60, the hot deck temperature setpoint is from 83.5 to When the OAT is 50 to 60, the cold deck temperature is from 59.5 to Enable the economizer when the OAT is in the temperature range from 38 to 65. When the OAT is from 40 to 60, the hot deck temperature setpoint is from 83.5 to When the OAT is from 50 to 60, the cold septoint is from 49 to deck temperature is from 59.5 to Enable the economizer when the OAT is Economizer None under the range from 38 to

165 AHU Group Group 8 SZ Group 9 SZ Group 10 MZ Group 11 MZ Table 5-46 continued Item Before CC Measures After CC Measures Economizer None Enable the economizer when the OAT is under the range from 38 to 65. Economizer None Enable the economizer when the OAT is under the range from 38 to 65. When the OAT is from 50 to 60, the Cold deck 57 temperature setpoint is from 59.5 to Hot deck When the OAT is from 45 When the OAT is from 40 to 60, the hot to 65, the temperature deck temperature setpoint is from 78.5 to setpoint is from 81 to Enable the economizer when the OAT is Economizer From 38 to 58 under the temperature range from 38 to 65. Cold deck 57 When the OAT is 50 to 60, the cold deck temperature is 59.5 to When the OAT is from 50 Hot deck to 40, the temperature When the OAT is 40 to 60, the hot deck setpoint is from 83 to temperature setpoint is 78.5 to Enable the economizer when the OAT is Economizer None under the temperature range from 38 to 65. The coil temperatures applied to WinAM 4.3 have been edited based on the data in the CC report. The real project uses both the unoccupied model and the occupied model 148

166 for the coil temperature reset, and WinAM 4.3 doesn t have this option yet. The method used to solve this problem is to calculate the compromised temperature based on the ratio of the occupied time and the unoccupied time. Following is an example of the calculation of this temperature (Figure 5-10). Figure 5-10 Mixed air temperature setpoint (Bes-Tech Inc., and ESL. 2009a) Figure 5-10 shows the unoccupied hours are from 9:00 p.m. to 4:00 a.m. which total 7 hours, so the remaining 17 hours are the occupied time. The compromised temperature for cold deck upper limit will be: ( ) ( ) The compromised temperature for cold deck lower limit is: ( ) ( ) 149

167 The CC measures that cannot be applied to the WinAM 4.3 model are listed in Table 5-47: Table 5-47 CC measures that cannot be applied to the WinAM 4.3 model Item AHU Group Before CC Measures After CC Measures Occupied hours from 4:00 a.m. to Zone 7:00 p.m. temperature Based on manual Unoccupied hours are from 7:00 p.m. occupied and All adjustment to 4:00 a.m. During unoccupied time, unoccupied the heating temperature is 60 and mode the cooling temperature is 85. Economizers will be enabled when the Economizer All when the OAT is below 52. Some of the AHUs have OAT is lower than 65, and the the economizer mechanical cooling will be shut off When the OAT is 30, the relative Relative humidity ratio is 4. When the OAT humidity ratio Group 2 Doesn t mention is 50, the relative humidity ratio is reset 45%. The cold deck and the The cold deck and the hot deck hot deck temperature do Reset cold deck temperature setpoint are set not only Groups not have the relationship and hot deck based on the OAT but also based on 2,3,4 between the occupied temperature the occupied mode and the unoccupied mode and the mode. unoccupied mode. Reset mixed air Reset the mixed air temperature Group 2 Doesn t mention temperature setpoint based on the OAT. 150

168 Item Duct static pressure Supply temperature reset Condensed water temperature reset Boiler AHU Group Group 5 Group 6 Table 5-47 continued Before CC Measures After CC Measures Maintain the duct static pressure at its Doesn t mention setpoint and reset the duct pressure based on the fan speed. Reset the supply air temperature based Manually adjusted on the OAT and the room temperature. When the OAT is 70, the supply condensed water temperature is 85. Doesn t mention When the OAT is 75, the supply condensed water temperature is Steam pressure has been reduced, 2. heat exchanger pressure has been Doesn t mention reduced, and 3. supply hot water temperature has been reset Calibrate WinAM 4.3 model The calibration steps for this project are in Table AHU Group All AHU 1 Table 5-48 Calibrations for BACH Item Before Calibration After Calibration Lighting 1.2 Watts/ft Watts/ft2 Plug 2.2 Watts/ft Watts/ft2 Minimum airflow 0.3 CFM 0.4 CFM OA%

169 Table 5-48 continued AHU Group Item Before Calibration After Calibration AHU 2 Maximum airflow 1 CFM 1.4 CFM AHU 3 Minimum airflow 0.3 CFM 0.4 CFM AHU 4 Constant primary airflow 1 CFM 1.65 CFM OA% 10 7 AHU 5 Constant primary airflow 1 CFM 1.55 CFM AHU 6 Constant primary airflow 1 CFM 1.45 CFM OA% 10 7 AHU 7 Constant primary airflow 1 CFM 1.45 CFM AHU 8 Constant primary airflow 1 CFM 1.45 CFM AHU 9 Constant primary airflow 1 CFM 1.45 CFM AHU 10 Constant primary airflow 1 CFM 1.65 CFM OA% 10 7 AHU 11 Constant primary airflow 1 CFM 1.65 CFM Plant Electric cooling system efficiency 1 kw/ton 0.9 kw/ton Gas heating system efficiency 8 55% Apply CC measures to the calibrated model We apply the same CC measures discussed in Section to the calibrated WinAM 4.3 model b.1basemodel. The new model is named b.2basemodel. 152

170 6. RESULTS 6.1 Introduction This section focuses on the analysis of the results generated from the input parameter sensitivity analysis (Section 4) and the savings prediction experiment (Section 5). The method to detect the sensitivity for the selected 14 parameters is to calculate the yearly influence coefficient (IC) for each input based on monthly data. We then compare 14 IC values with each other within the same AHU system. The methods applied to analyze the savings prediction reliability results are the following. The savings were calculated after applying CC measures to the projects with and without calibration. The statistical methods NMBE and CV (RMSE) will be used for evaluating whether the model has been well-calibrated or not. According to ASHRAE Guideline 14, the model is considered well-calibrated if the NMBE is within and the CV(RMSE) is within when the model is calibrated with the monthly measured data. This allows us to figure out whether the well-calibrated model has the better predicted savings than the calibrated model. 6.2 Results for sensitivity analysis After analyzing the sensitivity of each parameter in Section 4, this section will focus on how the parameters affect the yearly energy consumptions. A yearly energy consumption IC value has been calculated for each input parameter. Figure 6-1 through 153

171 Figure 6-3 show the input parameter s effect on electric, chilled water and hot water consumption for the system with the economizer. Electric consumption Wall U, -1% Occ, -2% Tz, -53% Max, -6% IP, -4% Roof U, Window U, 2% NLPL, 15% FP, 11% Lighting, 48% Tc, 38% Min, 6% WW, 2% Influence coefficient Figure 6-1 IC for electric consumption with the economizer 154

172 Chilled water consumption Wall U, -4% Occ, -11% Tc, -99% Tz, -118% IP, -12% Roof U, 2% Window U, 15% NLPL, 2% FP, 4% Lighting, 14% Max, 15% Min, 25% WW, 13% Influence coefficient Figure 6-2 IC for chilled water consumption with the economizer Hot water consumption Wall U, -13% NLPL, -14% FP, Lighting, -11% Tc, -149% Roof U, 3% Window U, 48% Occ, 6% Tz, 681% Max, Min, 66% WW, 43% IP, 13% Influence coefficient Figure 6-3 IC for hot water consumption with the economizer 155

173 Figure 6-4 through Figure 6-6 show the input parameter s effect on electric, chilled water and hot water consumption for the system without the economizer. Electric consumption Wall U, -1% Occ, -2% Tz, -53% Max, -6% IP, -4% Roof U, Window U, 2% NLPL, 15% FP, 11% Lighting, 48% Tc, 38% Min, 6% WW, 2% OA, Influence coefficient Figure 6-4 IC for electric consumption without the economizer 156

174 Chilled water consumption Wall U, -4% Occ, -12% 1 Tc, -101% Tz, -69% IP, -11% Roof U, 1% Window U, 14% NLPL, 2% FP, 4% Lighting, 14% Max, 13% Min, 3 WW, 12% OA, 15% Influence coefficient Figure 6-5 IC for chilled water consumption without the economizer Hot water consumption Wall U, -13% NLPL, -14% Lighting, -12% Tc, -169% Roof U, 3% Window U, 48% FP, Occ, 6% Tz, 684% Max, Min, 66% WW, 43% IP, 13% OA, Influence coefficient Figure 6-6 IC for hot water consumption without the economizer 157

175 Table 6-1 Summary of IC for each parameter s sensitivity to different energy recourses in the SDVAV system with the economizer Rank No. Electric Chilled water Hot water 1-53%* Tz -118%* Tz 681%* Tz 2 48% Lighting -99%* Tc 149%* Tc 3 38%* Tc 25% Min 66% Min 4 15% NLPL 2 OA 48% Window U 5 11% FP 15% Max 43% WW 6 6% Max 15% Window U 14% NLPL 7-6% Min 15% Lighting 13% IP 8-4% IP 13% WW 13% Wall U 9 2% WW -12% IP 11% Lighting 10 2% Window U -11% Occ 6% Occ 11-2% Occ 4% FP 3% Roof U 12-1% Wall U -4% Wall U Max 13 Roof U 2% NLPL FP 14 OA 2% Roof U OA Table 6-1 provides a summary for ranking the sensitivity of analyzed inputs for the SDVAV system with the economizer. Table 6-2 shows the yearly EPs for each parameter under electric consumption, chilled water consumption and hot water consumption. Column one in Table 6-2 is the same rank number in Table 6-1. In this way, Table 6-2 gives the information for the average energy consumption difference between the adjusted models and the baseline model. The results for Tz and Tc have an * in Table 6-1 to Table 6-4 because the average results are not obtained from inputs with the full 3 range. Engineers can decide which input parameter they will spend more time for the accrete prediction result based on Table 6-1 and Table 6-2 for the SDVAV system with the economizer. 158

176 Table 6-2 Summary of absolute EPs for each parameter compare with baseline model to different energy recourses in SDVAV system with the economizer IC value rank No. Electric consumption Chilled water consumption Hot water consumption 1 6%* Tz 14%* Tz 67%* Tz 2 11% Lighting 21%* Tc 27%* Tc 3 7%* Tc 5% Min 15% Min 4 3% NLPL 4% OA 11% Window U 5 2% FP 4% Max 9% WW 6 1% Max 3% Window U 3% NLPL 7 1% Min 3% WW 3% IP 8 1% IP 1% IP 3% Wall U 9 WW 3% OCC 2% Lighting 10 Window U 3% Lighting 1% Occ 11 Occ 1% FP 1% Roof U 12 Wall U 1% Wall U Max 13 Roof U NLPL FP 14 OA Roof U OA Table 6-3 Summary of IC for each parameter s sensitivity to different energy recourses in SDVAV system without the economizer Rank No. Electric consumption Chilled water consumption Hot water consumption 1-53%* Tz -101%* Tc 684%* Tz 2 48% Lighting -69%* Tz -169%* Tc 3 38%* Tc 3 Min 66% Min 4 15% NLPL 15% OA 48% Window U 5 11% FP 14% Lighting 43% WW 6 6% Min 14% Window U -14% NLPL 159

177 Table 6-3 continued Rank No. Electric consumption Chilled water consumption Hot water consumption 7-6% Max 13% Max 13% IP 8-4% IP 12% WW -13% Wall U 9 2% Window U -12% Occ -12% Lighting 10 2% WW -11% IP 6% Occ 11-2% Occ 4% FP 3% Roof U 12-1% Wall U -4% Wall U OA 13 Roof U 2% NLPL Max 14 OA 1% Roof U FP Table 6-1 and Table 6-3 provide IC summaries for both the SDVAV system with the economizer and the system without the economizer. The highly sensitive parameters for yearly electric and hot water consumption are the same. Table 6-4 Summary of absolute EPs for each parameter compared with the baseline model to different energy recourses in SDVAV system without the economizer IC value rank No. Electric consumption Chilled water consumption Hot water consumption 1 6%* Tz 19%* Tc* 97%* Tz* 2 1 Lighting 7%* Tz* 3* Tc* 3 8%* Tc 6% Min 13% Min 4 3% NLPL 3% OA 1 Window U 5 2% FP 3% Lighting 9% WW 6 1% Min Window U 3% NLPL 160

178 Table 6-4 continued IC value rank No. Electric consumption Chilled water consumption Hot water consumption 7 1% Max 3% Max 3% IP 8 1% IP 3% WW 3% Wall U 9 Window U 3% Occ 2% Lighting 10 WW 1% IP 1% Occ 11 Occ 1% FP 1% Roof U 12 Wall U 1% Wall U OA 13 Roof U NLPL Max 14 OA Roof U FP For each parameter, Table 6-4 provides a summary of yearly EPs for each parameter under electric consumption, chilled water consumption and hot water consumption. Like Table 6-2, it produces the EPs by adjusting the inputs for the SDVAV system without the economizer. The engineers can decide the error range they can accept for any energy recourse based on Table 6-4 and the sensitivity of each parameter in Table 6-3 to choose the best strategy to decide the inputs. This strategy will make the process of measuring each input more efficient. 6.3 Reliability of savings predictions Table 6-5 shows the total dollar savings percentage for each project. It includes the savings obtained from the model without calibration, the model with calibration and the real savings from CC reports. The predicted savings here are not based on applying the full CC measures to the WinAM 4.3 models, see Appendix B. The last two columns in 161

179 the table give the EPs between the measured savings and the simulated savings without and with calibration. Based on companions of the last two columns, the results from the simulated model with calibration is closer to the measured savings, except for the Sunset Valley Elementary School, the Blanchfield Army Community Hospital and the Austin City Hall models. The internal cooling and heating load calculated by the inputs generated from the CC report for the Sunset Valley Elementary School base model are over WinAM 4.3 s limitation. This causes an error in WinAM 4.3. Under this condition, the inputs are adjusted to run WinAM 4.3. This adjustment made this model a WinAM 4.3 uncalibrated model. The reason the predicted total energy savings percentage result obtained from the model with calibration is not as good as that obtained from the model without calibration for Blanchfield Army Community Hospital needs further research. The bottom line of Table 6-5 denotes that the predicted savings percentage from the calibrated model is closer to the real savings percentage. 162

180 Table 6-5 Dollar savings for each project Dollar Savings No. 1 Project Name Predicted savings Without With Calibration Calibration Real Savings Without Calibration Real Savings Deviation With Calibration Real Savings Austin City Hall 1 22% 17% -7% 5% 2 Dallas/Fort Worth International Airport Terminal D 3 DFW International Airport Rent-A-Car Center 4 4% 13% 17% -13% -4% 8% 9% 18% -1-9% DFW Terminal E 2 17% 9% 12% 9% 5 Sunset Valley Elementary School 6 DFW International Airport North Business Tower 7 Blanchfield Army Community Hospital 3 31% 21% 9% 1 5% 15% 13% -8% 2% 1 11% 9% 1% 2% Figure 6-7 and Figure 6-8 are the charts of the difference between the calculated savings from the simulated models and the calculated savings from the real models. The corresponding data are given in Table

181 After calibration savings % Before calibration savings % 35% 3 Real dollar savings vs. predicted dollar savings without calibration Austin City Hall 25% 2 15% 1 5% Real savings % DFW International Airport Terminal D DFW International Airport Rent- A-Car Center DFW International Airport Terminal E Sunset Valley Elementary School DFW International Airport North Business Tower Blanchfield Army Community Hospital Figure 6-7 Real dollar savings vs. predicted dollar savings without calibration 35% 3 Real dollar savings vs. predicted dollar savings with calibration Austin City Hall 25% 2 15% 1 5% Real savings % Figure 6-8 Real dollar savings vs. predicted dollar savings with calibration 164 DFW International Airport Terminal D DFW International Airport Rent- A-Car Center DFW International Airport Terminal E Sunset Valley Elementary School DFW International Airport North Business Tower Blanchfield Army Community Hospital

182 Figure 6-8 shows that almost all of the results from the models with calibration are closer to the real savings compared with the results in Figure 6-7, except for the two projects mentioned earlier. In general, calibrating the model allows the user to generate estimated savings that are closer to the measured savings. 165

183 7. CONCLUSIONS This research has involved the two main tasks for evaluating the performance of WinAM 4.3. Task 1 is sensitive analysis for the 14 parameters in WinAM 4.3, the other task is the predicted savings analysis. The 162 artificial energy models have been used for sensitivity analysis and seven real projects have been studied for predicted energy savings analysis. The 162 energy models were generated based on adjusting 14 parameters which were selected from WinAM4.3. The parameters are: 1) outside air percentage, 2) interior zone percentage, 3) window and wall ratio, 4) minimum airflow ratio, 5) maximum airflow ratio, 6) zone temperature setpoint, 7) cooling coil temperature setpoint, 8) lighting load, 9) fan power, 10) night plug load, 11) wall R-value, 12) window U-value, 13) roof U-value, and 14) occupancy. The seven real projects are: Austin City Hall (ACH) (Zhou et al. 2009) Blanchfield Army Community Hospital (BACH) (Bes-Tech Inc. and ESL 2009b) North Business Tower of DFW International Airport (ESL 2010b) Rent-A-Car Center of DFW International Airport (Zeig et al. 2004) Sunset Valley Elementary School (SVES) (Yagua et al. 2009) Terminal D of DFW International Airport (ESL 2010a) Terminal E of DFW International Airport (ESL 2010c) 166

184 7.1 Conclusion for sensitivity analysis From Table 6-1 and Table 6-3, the highly sensitive parameters for electric (fan power and lighting plug load) are the same for the system with the economizer and the system without the economizer. Although some of the parameters have different signs, the absolute value is the same (for example, the minimum airflow rate and maximum airflow rate parameters). Highly sensitive parameters include zone temperature setpoint, lighting load, cooling coil temperature, night lighting and plug load, and fan power. The highly sensitive parameters are also the same with or without the economizer for hot water consumption. In this case, the cooling coil temperature setpoint and zone temperature setpoint are also among the most highly sensitive parameters. In addition to these two parameters, which rank at the top in sensitivity, other highly sensitive parameters include the minimum airflow rate, window U-value, and window-wall ratio. The parameter sensitivity ranks are similar with or without the economizer for chilled water consumption. The zone temperature setpoint is the most sensitive parameter for the system with the economizer. The system without the economizer ranks zone temperature setpoint as the second most sensitive parameter, following the cooling coil temperature setpoint. In the system with the economizer, the cooling coil temperature setpoint is the most sensitive parameter. In both systems, the minimum airflow rate is the third most sensitive parameter. 167

185 From Table 6-2, the least sensitive parameters for electric consumption ( error percentage) are window-wall ratio, window U-value, peak occupancy, wall U-value, roof U-value, and outside air percentage for electric consumption. This is true for both the system with the economizer and the system without the economizer. Night light and plug load parameter and roof U-value are the least sensitive parameters for chilled water consumption. Maximum airflow rate, fan power and outside air percentage are the least sensitive parameters for hot water consumption. It is not necessary to detect the exact value of the least sensitive parameters; the estimated value can be used according to the WinAM 4.3 help manual: how to use WinAM to calculate savings from energy conservation measures (ESL 2013a). The engineer may thus devote more time to the more sensitive parameters. 7.2 Conclusion for predicted energy savings analysis ASHRAE Guideline 14 ( ASHRAE 2002) defines a well-calibrated model as having NMBE within 5% and the CV(RMSE) is within 15% when calibrated with monthly measured data. The lower the NMBE and CV(RMSE) are, the closer the calibrated model is to the real project. When comparing the NMBEs and CV(RMSE)s for the models without calibration in Table 7-1 with the deviations in Table 7-2, in each case, after calibration the values are reduced. This demonstrates how calibrating the model with WinAM 4.3 can improve the model s quality. 168

186 Table 7-1 NMBEs and CV(RMSE)s for models without calibration Project number Before Calibration Electric Chilled water Hot water CV CV CV CV CV CV NMBE (RMSE) NMBE (RMSE) NMBE (RMSE) NMBE (RMSE) NMBE (RMSE) NMBE (RMSE) NMBE % 32% 107% 10 13% 19% -6% 12% 76% 76% -48% 49% 57% 56% 41% 41% -6% 13% 32% 32% 43% 4 87% 87% 12% 41% -38% 59% 3 46% 77% 77% CV (RMSE) Table 7-2 NMBEs and CV(RMSE)s for models with calibration Project number After Calibration Electric Chilled water Hot water CV CV CV CV CV CV NMBE (RMSE) NMBE (RMSE) NMBE (RMSE) NMBE (RMSE) NMBE (RMSE) NMBE (RMSE) NMBE CV (RMSE) 1% 8% -13% 26% 5% 6% -3% 13% -7% 13% -1% 9% -27% 31% 3 33% -21% 57% -1% 7% 16% 2-23% 27% -23% 24% -1% 36% -46% 61% -14% 31% 17% 22% 169

187 From the comparison between the predicted savings and the real savings in Table 6-5, five of six calibrated model s deviations have been reduced, which implies the results from the predicted energy savings have been improved by calibrating the models with WinAM 4.3. The model for Sunset valley elementary school cannot be calibrated, as described in Section Future work For sensitivity analysis, suggestions for future work are: 1. Consider adjusting the parameters for more HVAC systems, for example, DDVAV system, DDCAV system, and SDCAV system. 2. Consider using different weather data for the same project, this research only uses the weather data from Austin, Texas. Applying different climate zone s data to the model is useful for generating highly sensitive parameters for each climate zone. 3. Consider adjusting the 14 parameters to the real project; in this way this methodology can give the direct instruction for the real CC project. For predicted energy savings analysis, suggestions for future work are: 1. Apply the same method to the newest version of WinAM 4.4, compare with the results obtained from WinAM 4.3. If the results have been improved compared with WinAM 4.3, this means that the new functions that have been applied to WinAM 4.4 work well. 170

188 2. Calculate the savings caused by the CC measures that cannot be applied to the WinAM 4.3 models. Include this energy savings in the predicted energy savings; in this way, the savings can be directly compared with the real savings. 171

189 REFERENCES ASHRAE ASHRAE Guideline , Measurement of Energy and Demand Savings. Atlanta, GA. Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. Bes-Tech Inc., and ESL. 2009a. Continuous Commissioning final report for Blanchfield Army Community Hospital, Fort Campbell, KY. Bes-Tech Inc, and ESL. 2009b. Continuous Commissioning Final Report for Bayne- Jones Army Community Hospital, Fort Polk, LA. Crawley, D. B EnergyPlus: New, capable, and linked. Architectural and Planning Research 21(4): Effinger, M., D. Song, and B. Juan-Carlos Continuous Commissioning Report for Fox Martin Army Community Hospital, Fort Benning, GA. ESL. 2010a. Continuous Commissioning of Terminal D, Energy Systems Laboratory, Texas Engineering Experiment Station, College Station, TX. ESL. 2010b. Continuous Commissioning of North Business Tower DFW International Airport final report, Energy Systems Laboratory, Texas Engineering Experiment Station, College Station, TX. ESL. 2010c. HVAC system imbalance at Terminal E and Continuous Commissioning of Terminal E, Energy Systems Laboratory, Texas Engineering Experiment Station, College Station, TX. ESL WinAM User's Manual, Version 4.3, Energy Systems Laboratory, Texas Engineering Experiment Station, College Station, TX. ESL. 2013a. How to use WinAM to calculate savings from energy conservation measures, Energy Systems Laboratory, Texas Engineering Experiment Station, College Station, TX. 172

190 ESL. 2013b. CC Compass online tools for Continuous Commissioning. Retrieved from Google Maps Blanchfield Army Community Hospital, Fort Campbell, Kentucky. Retrieved from =en&ll= , &spn= , &sll= , &sspn= , &hq=Blanchfield+Army+Community+Hosp ital&t=h&z=19&iwloc=a Helton, J. C Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliability Engineering and System Safety 91(10): Henninger, R. H., and M. J. Witte EnergyPlus Testing with ANSI/ASHRAE Standard (BESTEST). Park Ridge, IL: Gard Analytics. HHS Associates LLC, and ESL. 2009a. Continuous Commissioning for Tripler Army Medical Center, Honolulu, HI. HHS Associates LLC, and ESL. 2009b. Continuous Commissioning Follow-Up Report for Fox Army Health Center, Redstone Army Arsenal, Huntsville, AL. Hirsch, J. and Associates equest introductory tutorial (version 3.64). Camarillo, CA. Hopfe, C. J., J. L. M. Hensen, and W. Plokker Uncertainty and sensitivity analysis for detailed design support. Proceedings of 10th IBPSA Building Simulation Conference, Beijing: Tsinghua University, Hopfe, C. J., and J. L. M. Hensen Uncertainty analysis in building performance simulation for design support. Energy and Buildings 43(10): Lam, J. C Sensitivity analysis and energy conservation measures implications. Energy Conversion and Management 49(11): Litko, J. R Sensitivity analysis for robust parameter design experiments. Proceedings of the 37 th Conference on Winter Simulation, Winter Simulation Conference, Orlando, FL,

191 Liu, M., D. E. Claridge, W. D. Turner Continuous Commissioning guidebook: Maximizing building energy efficiency and comfort. Federal Energy Management Program (FEMP), U.S. Department of Energy. Lomas, K. J., and H. Eppel Sensitivity analysis techniques for building thermal simulation programs. Energy and Buildings 19(1): Petr, K., J. Filip, K. Kabele, and J. Hensen Technique of uncertainty and sensitivity analysis for sustainable building energy systems performance calculations. Proceedings of 10th IBPSA Building Simulation Conference, Tsinghua University, Beijing, Purdy, J., and I. Beausoleil-Morrison The significant factors in modelling residential buildings. Proceedings of Building Simulation, Seventh International Building Performance Simulation Association Conference, Rio de Janeiro, Brazil, Spitler, J. D., D. E. Fisher, and D. C. Zietlow A primer on the use of influence coefficients in building simulation. Proceedings of Building Simulation' 89 conference Struck, C An investigation of the option space in conceptual building design for advanced building simulation. Advanced Engineering Informatics 23(4): Struck, C., and J. Hensen Uncertainty analysis for conceptual building design A review of input data. Proceedings of the 1 st Int. IBPSA Germany/Austria Conf. BauSIM, October, Munich, Germany, Tian, W A review of sensitivity analysis methods in building energy analysis. Renewable and Sustainable Energy Reviews 20(0): Yagua, C., G. Napper, G. Wei, J. Baltazar-Cervantes, D. Turner, and D. Claridge Continuous Commissioning report for Delco Activity Center, Sunset Valley Elementary School, and Burnet Middle School, Energy Systems Laboratory, Texas Engineering Experiment Station, College Station,TX. Zeig, G., H. Huff, T. Giebler, K. Milligan, J. Baltazar-Cevantes, G. Wei, B. Yazdani, and D. Turner Technical assistance report for DFW International Airport Rent- A-Car Center and Terminal B, Energy Systems Laboratory, Texas Engineering 174

192 Experiment Station, College Station, TX. Zhou, J., C. Yagua, G. Wei, J. Baltazar-Cevantes, S. Deng, D. Turner, M. Verdict, and D. Claridge Continuous Commissioning report and BAS sequence of operation for Austin City Hall, Energy Systems Laboratory, Texas Engineering Experiment Station, College Station, TX. 175

193 APPENDIX A For making the comparison between equest and WinAM as close as possible, the weather data used in WinAM should be used in equest. The steps for generating the weather data for equest are as follows: A.1 Convert the.bin weather file into a.ft weather file The user needs to download DOE22WeatherUtilities.zip from the website After downloading, unzip this file to any local root hard drive. Create a new folder and name it WEATHER in C:\DOE22. Copy the weather file in TMY2 to the WEATHER file in DOE22. The user than needs to run the DOS command box. The method to run the DOS command box is: From Start button, select Run. In the jumped out window input cmd and click OK in the DOS command box. Get in to C:\ first by inputting cd C:\ and launch into C:\DOE22\UTIL32 by typing cd \DOE22\UTIL

194 To convert the weather file successfully, the user needs the tool named MKAFT.bat ( make a.ft file, ft is the extension which means the file is the unzipped DOE2-2/eQUEST weather file). If the user named the.bin weather file for example Austin.bin, then the user needs to input mkaft Austin in the DOS command window, e.g. C:\DOE22\UTIL32\>mkaft Austin. In this way, users will get the unzipped weather file to check. The reason to do this step first is that the experiment in this research needs to keep the dry bulb temperature, wet bulb temperature and dew point temperature exactly the same both in equest and WinAM. A.2 Convert the.ft weather file into an.epw weather file EnergyPlus is the required download software for converting weather files. The software offered by EnergyPlus entitiled Weather Statistics and Conversions will be used. The initial interface is shown in Figure A-1 177

195 Figure A-1 Initial interface of weather statistics and conversions The steps for using this software are: Click Select File to Convert button Choose select output format as EnergyPlus weather format (EPW) and decide where will be the proper place to save the new weather file, see Figure A-2. After that, the conversion can be done. 178

196 Figure A-2 After applying the conversion command A.3 Convert.epw weather file into a.cvs weather file Repeat the process in step A.2 Convert the.ft weather file into an.epw weather file. Then convert the.epw file into a. cvs file. After setting up the.cvs weather file, retrieve the weather data used for WinAM. Obtain the weather data from the CC-compass website, and convert the units used in the WinAM weather file from Fahrenheit to Celsius before copying the dry bulb temperature and dew point temperature to the converted.cvs file. Delete the other nonrelative information in the converted.cvs weather file. The purpose is to have exactly the same weather file that can be used for both WinAM and equest. 179

197 There is no other weather information in the weather file used for WinAM, e.g. wind speed, solar direction etc. The.bin file used for equest has this information. A.4 Convert edited.cvs file back into an.epw file This method is similar to step A.2 Convert the.ft weather file into an.epw weather file. The difference compare with step A.2 is pick up the output format with the one has.epw option. A.5 Convert edited.epw weather file into a.bin weather file. Software DOE-2 processor is required here. After this process, the edited weather file is ready to be used. 180

198 Figure A-3 DOE-2 Processor 181